Continuous non-invasive monitoring of a pregnant human subject

ABSTRACT

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/389,618, filed Dec. 23, 2016, which is continuation of U.S. patentapplication Ser. No. 15/072,051, filed Mar. 16, 2016, patented on Feb.21, 2017 and issued U.S. Pat. No. 9,572,504, which is acontinuation-in-part of U.S. patent application Ser. No. 14/921,489,filed on Oct. 23, 2015, patented on Jul. 19, 2016 and issued U.S. Pat.No. 9,392,952, which claims priority to U.S. Provisional PatentApplication Ser. No. 62/133,485, filed on Mar. 16, 2015, the entirecontents of which are incorporated by reference in their entirety.

FIELD OF THE INVENTION

This invention relates to apparatuses, systems and methods forcontinuous, non-invasive monitoring of a fetus and/or mother using frommultiple sensors that collect maternal and fetal cardiac signals data.

BACKGROUND

Monitoring maternal and fetal cardiac activity can be useful todetermine of the health of a fetus and the mother during pregnancy.

SUMMARY

In one embodiment, the present invention provides a garment thatincludes:

-   -   a) at least one electrocardiogram sensor configured to contact        the skin of the abdomen of a pregnant human subject and detect        fetal and maternal cardiac electrical activity;    -   b) at least one acoustic sensor configured to contact the skin        of the abdomen of a pregnant human subject and detect fetal and        maternal cardiac electrical activity; and    -   c) a garment configured to position and contact the at least one        electrocardiogram sensor and the at least one acoustic sensor on        the abdomen of the pregnant human subject.

In one embodiment, the garment is further configured to include:

-   -   d) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving raw        Electrocardiogram (ECG) signals data from the at least one pair        of ECG sensors; wherein the at least one pair of ECG sensors is        positioned in on an abdomen of a pregnant human subject; wherein        the raw ECG signals data comprise data representative of a N        number of raw ECG signals (raw N-ECG signals data) which are        being acquired in real-time from the at least one pair of ECG        sensors; digital signal filtering the raw ECG signals data to        form filtered N-ECG signals data having filtered N-ECG signals;        detecting maternal heart peaks in each of the filtered N-ECG        signal in the filtered N-ECG signals data; subtracting, from        each of the filtered N-ECG signal of the filtered N-ECG signals        data, the maternal ECG signal, by utilizing at least one        non-linear subtraction procedure to obtain corrected ECG signals        data which comprise data representative of a N number of        corrected ECG signals (corrected N-ECG signals data), wherein        the at least one non-linear subtraction procedure comprises:        iteratively performing: i) dividing each filtered N-ECG signal        of N-ECG signals of the filtered N-ECG signals data into a        second plurality of ECG signal segments,) wherein each ECG        signal segment of the plurality of ECG signal segments        corresponds to a beat interval of a full heartbeat, and 2)        wherein each beat interval is automatically determined based, at        least in part on automatically detecting an onset value and an        offset value of such beat interval; ii) modifying each of the        plurality of filtered N-ECG signal segments to form a plurality        of modified filtered N-ECG signal segments, wherein the        modifying is performed using at least one inverse optimization        scheme based on a set of parameters, wherein values of the set        of parameters is determined based on: iteratively performing: 1)        defining a global template based on a standard heartbeat profile        of an adult human being; 2) setting a set of tentative values        for a local template for each filtered N-ECG signal segment;        and 3) utilizing at least one optimization scheme to determine        an adaptive template for each filtered N-ECG signal segment        based on the local template being matched to the global template        within a pre-determined similarity value; and iii) eliminating        the modified segments from each of the filtered N-ECG signals,        by subtracting the adaptive template from the filtered N-ECG        signal thereby generating each corrected ECG signal; extracting        raw fetal ECG signals data from the filtered N-ECG signals data        based on the corrected ECG signals data, wherein the raw fetal        ECG signals data comprises a N number of fetal ECG signals (raw        N-ECG fetal signals data); processing the raw N-ECG fetal        signals data to improve a signal-to-noise ratio of the N-ECG        fetal signals to form filtered N-ECG fetal signals data;        detecting fetal heart peaks in the filtered N-ECG fetal signals        data; calculating, based on detected fetal heart peaks, at least        one of: i) fetal heart rate, ii) fetal heart curve, iii)        beat-2-beat fetal heart rate, or iv) fetal heart rate        variability; and outputting a result of the calculating        operation;    -   e) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving, by at        least one computer processor executing specific programmable        instructions configured for the method, a plurality of        Phonocardiogram (PCG) signals data inputs from a plurality of        acoustic sensors; digital signal filtering, by the at least one        computer processor, utilizing a plurality of bandpass filters,        the plurality of PCG signals data inputs to form a plurality of        filtered PCG outputs, wherein the plurality of bandpass filters        comprises a L number of bandpass filters, wherein each bandpass        filter outputs a K number of filtered PCG outputs; wavelet        denoising, by the at least one computer processor, a first        subset of filtered PCG outputs of the plurality of filtered PCG        outputs to form a M number of denoised filtered PCG outputs,        wherein M is equal to L multiply by K; transforming, by the at        least one computer processor, utilizing an        Independent-Component-Analysis (ICA), a second subset of        filtered PCG outputs of the plurality of filtered PCG outputs to        form the M number of filtered ICA transforms; transforming, by        the at least one computer processor, utilizing the        Independent-Component-Analysis (ICA), a first portion of the        second subset of denoised filtered PCG outputs to form the M        number of denoised filtered ICA transforms; compiling, by the at        least one computer processor, a S number of a plurality of        detection heartbeat (DH) inputs, comprising: i) the M number of        filtered PCG outputs, ii) the M number of the denoised filtered        PCG outputs, iii) the M number of the filtered ICA transforms,        and iv) the M number of the denoised filtered ICA transforms;        detecting, by the at least one computer processor, beat        locations of beats in each of DH inputs; calculating, by the at        least one computer processor, a confidence score that describes        a probability that the beats in each DH input of the plurality        of DH inputs represent actual heartbeats and not a noise;        dividing, by the at least one computer processor, the plurality        of DH inputs into at least two groups: i) a first group of DH        inputs containing fetal heartbeats, ii) a second group of DH        inputs containing maternal heartbeats; selecting, by the at        least one computer processor, from the first group of DH inputs,        at least one particular fetal DH input that contains the fetal        heartbeat based on a first confidence score of the at least one        particular fetal DH input; and selecting, by the at least one        computer processor, from the second group of DH inputs, at least        one particular maternal DH input that contains the maternal        heartbeat, based on a second confidence score of the at least        one particular maternal DH input;    -   f) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;    -   v. based on the consolidated heart rate and scores for the        plurality of time points over the particular time interval,        generating, by the at least one computer processor, a fetal        heart rate probability mesh;    -   vi. based on the fetal heart rate probability mesh, generating,        by the at least one computer processor, an estimated fetal heart        rate over the particular time interval,        -   wherein the estimated fetal heart rate over the particular            time interval is calculated based on (1) cost representing            fetal heart probability mesh values at each point of the            estimated fetal heart rate over the particular time            interval; and (2) cost representing the overall tortuosity            of the estimated fetal heart rate over the particular time            interval.

In one embodiment, the garment is a belt.

In one embodiment, the present invention provides a system formonitoring maternal and fetal cardiac activity that includes:

-   -   a) at least one electrocardiogram sensor configured to contact        the skin of the abdomen of a pregnant human subject and detect        fetal and maternal cardiac electrical activity;    -   b) at least one acoustic sensor configured to contact the skin        of the abdomen of a pregnant human subject and detect fetal and        maternal cardiac electrical activity;    -   c) a garment configured to position and contact the at least one        electrocardiogram sensor and the at least one acoustic sensor on        the abdomen of the pregnant human subject;    -   d) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving raw        Electrocardiogram (ECG) signals data from the at least one pair        of ECG sensors; wherein the at least one pair of ECG sensors is        positioned in on an abdomen of a pregnant human subject; wherein        the raw ECG signals data comprise data representative of a N        number of raw ECG signals (raw N-ECG signals data) which are        being acquired in real-time from the at least one pair of ECG        sensors; digital signal filtering the raw ECG signals data to        form filtered N-ECG signals data having filtered N-ECG signals;        detecting maternal heart peaks in each of the filtered N-ECG        signal in the filtered N-ECG signals data; subtracting, from        each of the filtered N-ECG signal of the filtered N-ECG signals        data, the maternal ECG signal, by utilizing at least one        non-linear subtraction procedure to obtain corrected ECG signals        data which comprise data representative of a N number of        corrected ECG signals (corrected N-ECG signals data), wherein        the at least one non-linear subtraction procedure comprises:        iteratively performing: i) dividing each filtered N-ECG signal        of N-ECG signals of the filtered N-ECG signals data into a        second plurality of ECG signal segments,) wherein each ECG        signal segment of the plurality of ECG signal segments        corresponds to a beat interval of a full heartbeat, and 2)        wherein each beat interval is automatically determined based, at        least in part on automatically detecting an onset value and an        offset value of such beat interval; ii) modifying each of the        plurality of filtered N-ECG signal segments to form a plurality        of modified filtered N-ECG signal segments, wherein the        modifying is performed using at least one inverse optimization        scheme based on a set of parameters, wherein values of the set        of parameters is determined based on: iteratively performing: 1)        defining a global template based on a standard heartbeat profile        of an adult human being; 2) setting a set of tentative values        for a local template for each filtered N-ECG signal segment;        and 3) utilizing at least one optimization scheme to determine        an adaptive template for each filtered N-ECG signal segment        based on the local template being matched to the global template        within a pre-determined similarity value; and iii) eliminating        the modified segments from each of the filtered N-ECG signals,        by subtracting the adaptive template from the filtered N-ECG        signal thereby generating each corrected ECG signal; extracting        raw fetal ECG signals data from the filtered N-ECG signals data        based on the corrected ECG signals data, wherein the raw fetal        ECG signals data comprises a N number of fetal ECG signals (raw        N-ECG fetal signals data); processing the raw N-ECG fetal        signals data to improve a signal-to-noise ratio of the N-ECG        fetal signals to form filtered N-ECG fetal signals data;        detecting fetal heart peaks in the filtered N-ECG fetal signals        data; calculating, based on detected fetal heart peaks, at least        one of: i) fetal heart rate, ii) fetal heart curve, iii)        beat-2-beat fetal heart rate, or iv) fetal heart rate        variability; and outputting a result of the calculating        operation;    -   e) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving, by at        least one computer processor executing specific programmable        instructions configured for the method, a plurality of        Phonocardiogram (PCG) signals data inputs from a plurality of        acoustic sensors; digital signal filtering, by the at least one        computer processor, utilizing a plurality of bandpass filters,        the plurality of PCG signals data inputs to form a plurality of        filtered PCG outputs, wherein the plurality of bandpass filters        comprises a L number of bandpass filters, wherein each bandpass        filter outputs a K number of filtered PCG outputs; wavelet        denoising, by the at least one computer processor, a first        subset of filtered PCG outputs of the plurality of filtered PCG        outputs to form a M number of denoised filtered PCG outputs,        wherein M is equal to L multiply by K; transforming, by the at        least one computer processor, utilizing an        Independent-Component-Analysis (ICA), a second subset of        filtered PCG outputs of the plurality of filtered PCG outputs to        form the M number of filtered ICA transforms; transforming, by        the at least one computer processor, utilizing the        Independent-Component-Analysis (ICA), a first portion of the        second subset of denoised filtered PCG outputs to form the M        number of denoised filtered ICA transforms; compiling, by the at        least one computer processor, a S number of a plurality of        detection heartbeat (DH) inputs, comprising: i) the M number of        filtered PCG outputs, ii) the M number of the denoised filtered        PCG outputs, iii) the M number of the filtered ICA transforms,        and iv) the M number of the denoised filtered ICA transforms;        detecting, by the at least one computer processor, beat        locations of beats in each of DH inputs; calculating, by the at        least one computer processor, a confidence score that describes        a probability that the beats in each DH input of the plurality        of DH inputs represent actual heartbeats and not a noise;        dividing, by the at least one computer processor, the plurality        of DH inputs into at least two groups: i) a first group of DH        inputs containing fetal heartbeats, ii) a second group of DH        inputs containing maternal heartbeats; selecting, by the at        least one computer processor, from the first group of DH inputs,        at least one particular fetal DH input that contains the fetal        heartbeat based on a first confidence score of the at least one        particular fetal DH input; and selecting, by the at least one        computer processor, from the second group of DH inputs, at least        one particular maternal DH input that contains the maternal        heartbeat, based on a second confidence score of the at least        one particular maternal DH input;    -   f) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid, determining;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval,            -   wherein the estimated fetal heart rate over the                particular time interval is calculated based on (1) cost                representing fetal heart probability mesh values at each                point of the estimated fetal heart rate over the                particular time interval; and (2) cost representing the                overall tortuosity of the estimated fetal heart rate                over the particular time interval.

In one embodiment, the present invention provides a system forgenerating an estimation of fetal cardiac activity that includes:

-   -   a) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval,            -   wherein the estimated fetal heart rate over the                particular time interval is calculated based on (1) cost                representing fetal heart probability mesh values at each                point of the estimated fetal heart rate over the                particular time interval; and (2) cost representing the                overall tortuosity of the estimated fetal heart rate                over the particular time interval.

In one embodiment, cost representing fetal heart probability mesh valuesat each point of the estimated fetal heart rate over the particular timeinterval; and cost representing the overall tortuosity of the estimatedfetal heart rate over the particular time interval is performed, by theat least one computer processor, using dynamic programming, where eachvalue of the accumulated cost mesh is calculated as a sum of the fetalheart rate probability mesh value at that point and of the minimal pathin its neighborhood in the previous step, based on:

E(i,j)=e(i,j)+min(E(i−1,j−k)); k=−4:4

where:

-   -   e is the value of the fetal heart rate probability mesh.    -   E is the accumulated cost.    -   i represents time, j represents heart rate values in        neighborhood, +/−4 bpm/second.

In one embodiment, cost representing fetal heart probability mesh valuesat each point of the estimated fetal heart rate over the particular timeinterval; and cost representing the overall tortuosity of the estimatedfetal heart rate over the particular time interval is performed, by theat least one computer processor, using an exhaustive search.

In one embodiment, the present invention provides a computer implementedmethod that includes:

-   -   a) receiving raw Electrocardiogram (ECG) signals data from the        at least one pair of ECG sensors; wherein the at least one pair        of ECG sensors is positioned in on an abdomen of a pregnant        human subject; wherein the raw ECG signals data comprise data        representative of a N number of raw ECG signals (raw N-ECG        signals data) which are being acquired in real-time from the at        least one pair of ECG sensors; digital signal filtering the raw        ECG signals data to form filtered N-ECG signals data having        filtered N-ECG signals; detecting maternal heart peaks in each        of the filtered N-ECG signal in the filtered N-ECG signals data;        subtracting, from each of the filtered N-ECG signal of the        filtered N-ECG signals data, the maternal ECG signal, by        utilizing at least one non-linear subtraction procedure to        obtain corrected ECG signals data which comprise data        representative of a N number of corrected ECG signals (corrected        N-ECG signals data), wherein the at least one non-linear        subtraction procedure comprises: iteratively performing: i)        dividing each filtered N-ECG signal of N-ECG signals of the        filtered N-ECG signals data into a second plurality of ECG        signal segments,) wherein each ECG signal segment of the        plurality of ECG signal segments corresponds to a beat interval        of a full heartbeat, and 2) wherein each beat interval is        automatically determined based, at least in part on        automatically detecting an onset value and an offset value of        such beat interval; ii) modifying each of the plurality of        filtered N-ECG signal segments to form a plurality of modified        filtered N-ECG signal segments, wherein the modifying is        performed using at least one inverse optimization scheme based        on a set of parameters, wherein values of the set of parameters        is determined based on: iteratively performing: 1) defining a        global template based on a standard heartbeat profile of an        adult human being; 2) setting a set of tentative values for a        local template for each filtered N-ECG signal segment; and 3)        utilizing at least one optimization scheme to determine an        adaptive template for each filtered N-ECG signal segment based        on the local template being matched to the global template        within a pre-determined similarity value; and iii) eliminating        the modified segments from each of the filtered N-ECG signals,        by subtracting the adaptive template from the filtered N-ECG        signal thereby generating each corrected ECG signal; extracting        raw fetal ECG signals data from the filtered N-ECG signals data        based on the corrected ECG signals data, wherein the raw fetal        ECG signals data comprises a N number of fetal ECG signals (raw        N-ECG fetal signals data); processing the raw N-ECG fetal        signals data to improve a signal-to-noise ratio of the N-ECG        fetal signals to form filtered N-ECG fetal signals data;        detecting fetal heart peaks in the filtered N-ECG fetal signals        data; calculating, based on detected fetal heart peaks, at least        one of: i) fetal heart rate, ii) fetal heart curve, iii)        beat-2-beat fetal heart rate, or iv) fetal heart rate        variability; and outputting a result of the calculating        operation;    -   b) receiving, by at least one computer processor executing        specific programmable instructions configured for the method, a        plurality of Phonocardiogram (PCG) signals data inputs from a        plurality of acoustic sensors; digital signal filtering, by the        at least one computer processor, utilizing a plurality of        bandpass filters, the plurality of PCG signals data inputs to        form a plurality of filtered PCG outputs, wherein the plurality        of bandpass filters comprises a L number of bandpass filters,        wherein each bandpass filter outputs a K number of filtered PCG        outputs; wavelet denoising, by the at least one computer        processor, a first subset of filtered PCG outputs of the        plurality of filtered PCG outputs to form a M number of denoised        filtered PCG outputs, wherein M is equal to L multiply by K;        transforming, by the at least one computer processor, utilizing        an Independent-Component-Analysis (ICA), a second subset of        filtered PCG outputs of the plurality of filtered PCG outputs to        form the M number of filtered ICA transforms; transforming, by        the at least one computer processor, utilizing the        Independent-Component-Analysis (ICA), a first portion of the        second subset of denoised filtered PCG outputs to form the M        number of denoised filtered ICA transforms; compiling, by the at        least one computer processor, a S number of a plurality of        detection heartbeat (DH) inputs, comprising: i) the M number of        filtered PCG outputs, ii) the M number of the denoised filtered        PCG outputs, iii) the M number of the filtered ICA transforms,        and iv) the M number of the denoised filtered ICA transforms;        detecting, by the at least one computer processor, beat        locations of beats in each of DH inputs; calculating, by the at        least one computer processor, a confidence score that describes        a probability that the beats in each DH input of the plurality        of DH inputs represent actual heartbeats and not a noise;        dividing, by the at least one computer processor, the plurality        of DH inputs into at least two groups: i) a first group of DH        inputs containing fetal heartbeats, ii) a second group of DH        inputs containing maternal heartbeats; selecting, by the at        least one computer processor, from the first group of DH inputs,        at least one particular fetal DH input that contains the fetal        heartbeat based on a first confidence score of the at least one        particular fetal DH input; and selecting, by the at least one        computer processor, from the second group of DH inputs, at least        one particular maternal DH input that contains the maternal        heartbeat, based on a second confidence score of the at least        one particular maternal DH input;    -   c) performing the following operations on the results of step a        and b:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval, wherein the consolidated fetal heart rate and            score for an individual time point within the plurality of            time points is determined as one of the four options            selected from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval, wherein the estimated fetal heart rate over the            particular time interval is calculated based on (1) cost            representing fetal heart probability mesh values at each            point of the estimated fetal heart rate over the particular            time interval; and (2) cost representing the overall            tortuosity of the estimated fetal heart rate over the            particular time interval.

In one embodiment, the present invention provides a computer implementedmethod that includes:

-   -   a) receiving a calculated fetal heart rate for a plurality of        time points over a particular time interval from filtered N-ECG        fetal signals data and a calculated fetal heart rate for a        plurality of time points over a particular time interval from        filtered PCG outputs;    -   b) determining the score of the calculated fetal heart rate for        the plurality of time points over the particular time interval        for the filtered N-ECG fetal signals;    -   c) determining the score of the calculated fetal heart rate for        the plurality of time points over the particular time interval        for the filtered PCG outputs;    -   d) based on the calculated fetal heart rate and score for a        plurality of time points over a particular time interval from        filtered N-ECG fetal signals data, and the calculated fetal        heart rate and score for a plurality of time points over a        particular time interval from filtered PCG outputs, determining        a consolidated fetal heart rate and score for the plurality of        time points over the particular time interval,        -   wherein the consolidated fetal heart rate and score for an            individual time point within the plurality of time points is            determined as one of the four options selected from the            group consisting of:        -   1. the weighted average of the calculated heart rate from            the filtered N-ECG fetal signals data and the filtered PCG            outputs for the individual time point, if the calculated            heart rate from the filtered N-ECG fetal signals data and            the filtered PCG outputs for the individual time point            differs by 10 beats per minute or less, and if the scores of            the calculated fetal heart rate for the individual time            point for both the filtered N-ECG fetal signals data and the            filtered PCG outputs are valid;        -   2. the calculated heart rate having the lower score, if the            calculated heart rate from the filtered N-ECG fetal signals            data and the filtered PCG outputs for the individual time            point differs by more than 10 beats per minute, and if the            scores of the calculated fetal heart rate for the individual            time point for both the filtered N-ECG fetal signals data            and the filtered PCG outputs are valid;        -   3. the calculated heart rate that has the valid score; and        -   4. no consolidated fetal heart rate and score, if neither            the calculated heart rate from the filtered N-ECG fetal            signals data or the filtered PCG outputs has a valid score;    -   e) based on the consolidated heart rate and scores for the        plurality of time points over the particular time interval,        generating, by the at least one computer processor, a fetal        heart rate probability mesh;    -   f) based on the fetal heart rate probability mesh, generating,        by the at least one computer processor, an estimated fetal heart        rate over the particular time interval,        -   wherein the estimated fetal heart rate over the particular            time interval is calculated based on (1) cost representing            fetal heart probability mesh values at each point of the            estimated fetal heart rate over the particular time            interval; and (2) cost representing the overall tortuosity            of the estimated fetal heart rate over the particular time            interval.

In one embodiment, cost representing fetal heart probability mesh valuesat each point of the estimated fetal heart rate over the particular timeinterval; and cost representing the overall tortuosity of the estimatedfetal heart rate over the particular time interval is performed, by theat least one computer processor, using dynamic programming, where eachvalue of the accumulated cost mesh is calculated as a sum of the fetalheart rate probability mesh value at that point and of the minimal pathin its neighborhood in the previous step, based on:

E(i,j)=e(i,j)+min(E(i−1,j−k)); k=−4:4

where:

-   -   e is the value of the fetal heart rate probability mesh.    -   E is the accumulated cost.    -   i represents time, j represents heart rate values in        neighborhood, +/−4 bpm/second.

In one embodiment, cost representing fetal heart probability mesh valuesat each point of the estimated fetal heart rate over the particular timeinterval; and cost representing the overall tortuosity of the estimatedfetal heart rate over the particular time interval is performed, by theat least one computer processor, using an exhaustive search.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a representation of a system suitable for use in fetalheart rate monitoring systems according to some embodiments of thepresent invention.

FIG. 2 shows a first view of a garment according to some embodiments ofthe present invention being worn by a pregnant human subject.

FIG. 3 shows a front view of a garment according to some embodiments ofthe present invention.

FIG. 4 shows a rear view of a garment according to some embodiments ofthe present invention.

FIG. 5 shows an ECG sensor according to some embodiments of the presentinvention.

FIG. 6 shows an ECG sensor according to some embodiments of the presentinvention.

FIG. 7 shows an ECG sensor according to some embodiments of the presentinvention.

FIG. 8 shows an ECG sensor according to some embodiments of the presentinvention.

FIG. 9 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 10 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 11 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 12 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 13 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 14 shows a micrograph of electrically conductive fabric suitable asa cutaneous contact according to some embodiments of the presentinvention.

FIG. 15, panels a-c show recorded ECG signals data using electrodeserial nos. 3-5 respectively.

FIG. 16, panels a-d, show the recorded ECG signals data using electrodeserial nos. 3-5, and a control wet gel ECG electrode (GE Healthcare),respectively at 25 weeks from a pregnant human subject.

FIG. 17, panels a-d, show the recorded ECG signals data using electrodeserial nos. 3-5, and a control wet gel ECG electrode (GE), respectivelyat 25 weeks from a pregnant human subject.

FIG. 18 shows an experimental set up to determine surface resistivityand resistance of an electrically conductive fabric according to someembodiments of the present invention.

FIG. 19 shows an experimental set up to determine BTFT of anelectrically conductive fabric according to some embodiments of thepresent invention.

FIG. 20 shows a diagram if a skin-electrode interface equivalent circuitaccording to some embodiments of the present invention.

FIG. 21 shows a representation of a test electrode configuration.

FIG. 22 shows a body of an acoustic sensor according to some embodimentsof the present invention.

FIG. 23 shows a body of an acoustic sensor located within a housingaccording to some embodiments of the present invention.

FIG. 24 shows another housing according to some embodiments of thepresent invention.

FIG. 25 shows a structure configured to isolate the microphone fromacoustic signals not from the abdomen of the pregnant human subject.

FIG. 25 shows a representation of the transduction of acoustic signalsusing an acoustic sensor according to some embodiments of the presentinvention.

FIG. 27 shows the positions of the acoustic sensor (positions A1, A2,A3, A4, B1, B2, B3, B4, C1, C2, C3, C4) on the abdomen of the pregnanthuman subject according to some embodiments of the present invention.

FIG. 28 shows maternal acoustic signals data received using an acousticsensor according to some embodiments of the present invention.

FIG. 29 shows the positions of the at least one ECG sensor (positionsA1, A2, A3, A4, B1, B2, B3, B4) and the at least one acoustic sensor(positions M1, M2, M3 M4) on the abdomen of the pregnant human subject.

FIG. 30 shows a flow chart of an algorithm used to detect and analyzecardiac electrical activity data according to some embodiments of thepresent invention.

FIG. 31 shows an exemplary recording of cardiac electrical activity dataaccording to some embodiments of the present invention.

FIG. 32 shows a template maternal electrocardiogram according to someembodiments of the present invention.

FIG. 33 shows a flow chart of an algorithm used to perform maternalcardiac activity elimination according to some embodiments of thepresent invention.

FIG. 34 shows a flow chart of an algorithm used to perform maternalcardiac activity elimination according to some embodiments of thepresent invention.

FIG. 35 shows an overlay of a template maternal electrocardiogram over asingle maternal heart beat according to some embodiments of the presentinvention.

FIG. 36 shows an overlay of a template maternal electrocardiogram over asingle maternal heart beat according to some embodiments of the presentinvention.

FIG. 37 shows the result of maternal ECG elimination of according tosome embodiments of the present invention.

FIG. 38 shows a flow chart of an algorithm used to detect and analyzecardiac activity data according to some embodiments of the presentinvention.

FIGS. 39A and 39B show flow charts of a signal denoising algorithmaccording to some embodiments of the present invention.

FIGS. 40-44 show graphs illustrating results of processing of data fromacoustic sensors according to some embodiments of the present invention.

FIG. 45 shows a graphical display where the ECG signals data has “good”signal properties, but the PCG signals data has “poor” signalproperties.

FIG. 46 shows a graphical display where the PCG signals data has “good”signal properties, but the ECG signals data is “poor” shows a graphicaldisplay where the ECG signals data has “good” signal properties, but thePCG signals data is “poor”.

FIG. 47 shows a graphical display of the calculated fetal heart rate andscores for a plurality of time points over a particular time intervalfrom the filtered N-ECG fetal signals data and the calculated fetalheart rate for a plurality of time points over a particular timeinterval from the filtered PCG outputs Scores are displayed by themarker fullness—a fuller marker means a lower score, meaning higherconfidence in the single fetal heart rate estimation.

FIG. 48 shows a graphical display of the consolidated fetal heart rateand scores for a plurality of time points over a particular timeinterval. Scores are displayed by the marker fullness—a fuller markermeans a lower score, meaning higher confidence in the single fetal heartrate estimation.

FIG. 49 shows a fetal heart rate probability mesh, based on fetal heartrate estimation and score, as a function of heart rate (beats perminute) and time (seconds).

FIG. 50 shows a fetal heart rate probability mesh, based on fetal heartrate estimation and score, as a function of heart rate (beats perminute) and time (seconds).

FIG. 51 shows an estimated fetal heart rate over the particular timeinterval, calculated according to some embodiments of the presentinvention.

FIG. 52 shows a fetal heart rate estimation probability mesh accordingto some embodiments of the present invention.

FIG. 53 shows an accumulated cost mesh built according to someembodiments of the present invention.

FIG. 54 shows fetal heart rate estimation probability mesh according tosome embodiments of the present invention

FIG. 55 shows an accumulated cost mesh according to some embodiments ofthe present invention.

DETAILED DESCRIPTION

For clarity of disclosure, and not by way of limitation, the detaileddescription of the invention is divided into the following subsectionsthat describe or illustrate certain features, embodiments orapplications of the present invention.

As used herein the term “contact region” encompasses the contact areabetween the skin of a pregnant human subject and cutaneous contact i.e.the surface area through which current flow can pass between the skin ofthe pregnant human subject and the cutaneous contact.

The System According to Some Embodiments of the Present Invention:

This invention relates to apparatuses, systems and methods forcontinuous, non-invasive monitoring of a fetus and/or mother using frommultiple electrocardiogram sensors that collect maternal and fetalelectrical cardiac signals data and/or acoustic sensors that collectmaternal and fetal acoustic cardiac signals data.

Referring to FIG. 1, in some embodiments, the system for recording,detecting and analyzing fetal cardiac electrical activity comprises askin-electrode interface, at least one electrode, an analogpre-processing module, an analog to digital converter/microcontroller(ADC/MCU) module, a communications module, a smartphone module, and acloud computing module.

In some embodiments, the analog pre-processing module performs at leastone function selected from the group consisting of: amplification of therecorded signals, and filtering the recorded signals.

In some embodiments, the ADC/MCU module performs at least one taskselected from the group consisting of: converting analog signals todigital signals, converting the recorded signals to digital signals,compressing the data, digital filtering, and transferring the recordedelectrocardiogram signals data to the transmitter.

In some embodiments, the communications module transmits the recordedsignals to a wireless receiver.

In some embodiments, the system for recording, detecting and analyzingfetal cardiac electrical activity data regardless of sensor position,fetal orientation, fetal movement, or gestational age is the systemdisclosed in International Patent Application Serial No.PCT/IL2015/050407.

Without intending to be limited by any particular theory, cardiacactivity produces both acoustic and electrical signals. While theacoustic and electrical signals produced by cardiac activity may differ,they can be used together because they emanate from the same source(i.e. the feral and/or maternal heart).

However, the accuracy of fetal cardiac activity can be affected byseveral factors, such as, for example, fetal movement, the position ofthe fetal heart with respect to the at least one ECG electrode and/orthe at least one acoustic sensor, maternal muscular activity, maternalcardiac activity, maternal anatomy, placental position, and the like.

Without intending to be limited to any particular theory, the backgroundsignals can be, for example, scattered signals from the placenta, fetaland/or maternal gastric sounds, fetal and/or maternal skeletal muscleactivity, endometrial muscle activity, and the like.

For example, without intending to be limited to any particular theory,in some embodiments, the at least one ECG sensor and the at least oneacoustic sensor are in fixed positions. The fetus moves freely withinthe womb of the pregnant human subject, and is rarely in a fixedposition with respect to the sensors. This, together with the small sizeof the fetal heart, can (i) create artifacts in the signals data that isrecorded, and (ii) require sensitive sensors and signal processing todetect fetal signals from a constantly varying background signals.

Without intending to be limited to any particular theory, electricalsignals, such as fetal and maternal ECG signals and acoustic signals,such as fetal and maternal cardiac activity propagate differently. Forinstance, acoustic signals propagate well in fluid environments, suchas, for example, the amniotic fluid, with minimal scattering. As anotherexample, acoustic signals frequently have a lower signal to noise ratio,compared to ECG signals, possibly due to a lesser contribution of noiseinterference in ECG signals.

Thus, depending on the position of the fetus, the signal to noise ratioof the ECG signals and/or the acoustic signals may vary to a greater orlesser extent. For example, at one individual time point in a particulartime frame, the acoustic signal may be absent, whilst the ECG signal ispresent. Thus, in some embodiments, the system of the present inventionis capable of estimating fetal heart rate from either both fetal ECGsignals data and fetal acoustic cardiac signals data, or just fetal ECGsignals data, or just fetal acoustic cardiac signals data.

In the embodiments where the system of the present invention isestimates fetal heart rate from both fetal ECG signals data and fetalacoustic cardiac signals data, the relative contribution of either thefetal ECG signals data or the fetal acoustic cardiac signals data may beequal, or unequal.

In some embodiments, the system of the present invention is able tocontinuously fetal and maternal cardiac activity.

In some embodiments, the system of the present invention is used in ahospital setting. Alternatively, in some embodiments, the system of thepresent invention is used in a non-hospital setting.

In some embodiments, the system of the present invention, ECG signalsdata and or acoustic cardiac activity signals data is collected andanalyzed from a plurality of systems, to establish a database.

In some embodiments, the database is utilized in “big data analysis”,wherein the signals data collected from the system of the presentinvention by the pregnant human subject is compared to signals dataobtained from pregnant human subjects having particular demographicand/or geographic or other statistical properties. Without intending tobe limited to any particular theory, the comparison can allow a medicalpractitioner to advise the pregnant human subject to adjust diet,activity, medications, for example.

In some embodiments, the database is used by a healthcare professionaland/or the pregnant human subject to monitor fetal and/or maternalcardiac activity and well being. Alternatively, the database can be usedby the healthcare professional and/or the pregnant human subject todetect potentially harmful conditions, complications, or diseases duringpregnancy. In some embodiments, a system according to the presentinvention is capable of detecting potentially harmful conditions,complications, or diseases earlier than would be possible during routinecheck-ups during standard of care ante-natal practice.

In some embodiments, the system is used by the pregnant human subject toreduce pregnancy associated factors, such as, for example, stress andrisks related to nutrition, physical activity or lack thereof, sleep andother life-style factors that may affect the pregnancy.

In some embodiments, the pregnant human subject has been diagnosed as ahigh-risk pregnancy, and the system of the present invention is used inconjunction with a therapeutic treatment to treat or manage the highrisk pregnancy.

In some embodiments, the pregnant human subject has at least one diseaseor disorder selected from the group consisting of heart disease,hypotension, hypertension, low body weight, obesity, preeclampsia,gestational diabetes, thrombolytic complications and placental lesions,and the system of the present invention is used in conjunction with atherapeutic treatment to treat or manage the at least one disease ordisorder.

In some embodiments, the pregnant human subject has factors that areassociated with a high risk pregnancy, and the system of the presentinvention is used in conjunction with a therapeutic treatment to treator manage the high risk pregnancy.

In some embodiments, the garment according to some embodiments is wornby the pregnant human subject, wherein the garment is configured tocontinuously monitor fetal and maternal cardiac activity. In someembodiments, the fetal and maternal cardiac activity signals data istransmitted to a remote system, where the fetal and maternal cardiacactivity signals data is processed to output fetal and maternal heartrate.

In some embodiments, the system of the present invention is capable ofreceiving and processing additional data, such as, for example, maternalphysical activity, fetal kicks, maternal dietary intake, maternal healthinformation, mood, and the like. In some embodiments, the additionaldata is used, along with the fetal and maternal cardiac activity signalsdata to detect potentially harmful conditions, complications, ordiseases during pregnancy.

In some embodiments, the additional data is used, along with the fetaland maternal cardiac activity signals data to reduce pregnancyassociated factors, such as, for example, stress and risks related tonutrition, physical activity or lack thereof, sleep and other life-stylefactors that may affect the pregnancy.

Examples of fetal parameters according to some embodiments of thepresent invention is capable of monitoring include: cardiac activity,heart rate, cardiac vibro/acoustic sounds, pulse rate, heart ratevariability, bradycardia event, tachycardia event, desaturation, fetalmovement, fetal position/orientation, fetal sounds, fetal kicks, fetalbrain activity, fetal temperature, glucose, sleeping state, blood flow,blood pressure, or activity level.

Examples of maternal parameters according to some embodiments of thepresent invention is capable of monitoring include: cardiac activity,ejection fraction, heart rate, heart sounds, breathing depth andduration, pulse rate, heart rate variability, bradycardia event,tachycardia event, desaturation, brain activity, temperature, glucose,inflammation level, uterus activity, sleeping stages, blood pressure,physical activity level, posture, or body position.

In some embodiments, the system of the present invention is worn by thepregnant human subject for an extended period of time. The extendedperiod of time can be 24 hours or more. Alternatively, in someembodiments, the system of the present invention is worn by the pregnanthuman subject for 24 hours or less. Alternatively, in some embodiments,the system of the present invention is worn by the pregnant humansubject for 24 hours. Alternatively, in some embodiments, the system ofthe present invention is worn by the pregnant human subject for 12hours. Alternatively, in some embodiments, the system of the presentinvention is worn by the pregnant human subject for 11 hours.Alternatively, in some embodiments, the system of the present inventionis worn by the pregnant human subject for 10 hours. Alternatively, insome embodiments, the system of the present invention is worn by thepregnant human subject for 9 hours. Alternatively, in some embodiments,the system of the present invention is worn by the pregnant humansubject for 8 hours. Alternatively, in some embodiments, the system ofthe present invention is worn by the pregnant human subject for 7 hours.Alternatively, in some embodiments, the system of the present inventionis worn by the pregnant human subject for 6 hours. Alternatively, insome embodiments, the system of the present invention is worn by thepregnant human subject for 5 hours. Alternatively, in some embodiments,the system of the present invention is worn by the pregnant humansubject for 4 hours. Alternatively, in some embodiments, the system ofthe present invention is worn by the pregnant human subject for 3 hours.Alternatively, in some embodiments, the system of the present inventionis worn by the pregnant human subject for 2 hours. Alternatively, insome embodiments, the system of the present invention is worn by thepregnant human subject for 1 hour.

In one embodiment, the present invention provides a system formonitoring maternal and fetal cardiac activity that includes:

-   -   a) at least one electrocardiogram sensor configured to contact        the skin of the abdomen of a pregnant human subject and detect        fetal and maternal cardiac electrical activity;    -   b) at least one acoustic sensor configured to contact the skin        of the abdomen of a pregnant human subject and detect fetal and        maternal cardiac electrical activity;    -   c) a garment configured to position and contact the at least one        electrocardiogram sensor and the at least one acoustic sensor on        the abdomen of the pregnant human subject;    -   d) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving raw        Electrocardiogram (ECG) signals data from the at least one pair        of ECG sensors; wherein the at least one pair of ECG sensors is        positioned in on an abdomen of a pregnant human subject; wherein        the raw ECG signals data comprise data representative of a N        number of raw ECG signals (raw N-ECG signals data) which are        being acquired in real-time from the at least one pair of ECG        sensors; digital signal filtering the raw ECG signals data to        form filtered N-ECG signals data having filtered N-ECG signals;        detecting maternal heart peaks in each of the filtered N-ECG        signal in the filtered N-ECG signals data; subtracting, from        each of the filtered N-ECG signal of the filtered N-ECG signals        data, the maternal ECG signal, by utilizing at least one        non-linear subtraction procedure to obtain corrected ECG signals        data which comprise data representative of a N number of        corrected ECG signals (corrected N-ECG signals data), wherein        the at least one non-linear subtraction procedure comprises:        iteratively performing: i) dividing each filtered N-ECG signal        of N-ECG signals of the filtered N-ECG signals data into a        second plurality of ECG signal segments,) wherein each ECG        signal segment of the plurality of ECG signal segments        corresponds to a beat interval of a full heartbeat, and 2)        wherein each beat interval is automatically determined based, at        least in part on automatically detecting an onset value and an        offset value of such beat interval; ii) modifying each of the        plurality of filtered N-ECG signal segments to form a plurality        of modified filtered N-ECG signal segments, wherein the        modifying is performed using at least one inverse optimization        scheme based on a set of parameters, wherein values of the set        of parameters is determined based on: iteratively performing: 1)        defining a global template based on a standard heartbeat profile        of an adult human being; 2) setting a set of tentative values        for a local template for each filtered N-ECG signal segment;        and 3) utilizing at least one optimization scheme to determine        an adaptive template for each filtered N-ECG signal segment        based on the local template being matched to the global template        within a pre-determined similarity value; and iii) eliminating        the modified segments from each of the filtered N-ECG signals,        by subtracting the adaptive template from the filtered N-ECG        signal thereby generating each corrected ECG signal; extracting        raw fetal ECG signals data from the filtered N-ECG signals data        based on the corrected ECG signals data, wherein the raw fetal        ECG signals data comprises a N number of fetal ECG signals (raw        N-ECG fetal signals data); processing the raw N-ECG fetal        signals data to improve a signal-to-noise ratio of the N-ECG        fetal signals to form filtered N-ECG fetal signals data;        detecting fetal heart peaks in the filtered N-ECG fetal signals        data; calculating, based on detected fetal heart peaks, at least        one of: i) fetal heart rate, ii) fetal heart curve, iii)        beat-2-beat fetal heart rate, or iv) fetal heart rate        variability; and outputting a result of the calculating        operation;    -   e) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving, by at        least one computer processor executing specific programmable        instructions configured for the method, a plurality of        Phonocardiogram (PCG) signals data inputs from a plurality of        acoustic sensors; digital signal filtering, by the at least one        computer processor, utilizing a plurality of bandpass filters,        the plurality of PCG signals data inputs to form a plurality of        filtered PCG outputs, wherein the plurality of bandpass filters        comprises a L number of bandpass filters, wherein each bandpass        filter outputs a K number of filtered PCG outputs; wavelet        denoising, by the at least one computer processor, a first        subset of filtered PCG outputs of the plurality of filtered PCG        outputs to form a M number of denoised filtered PCG outputs,        wherein M is equal to L multiply by K; transforming, by the at        least one computer processor, utilizing an        Independent-Component-Analysis (ICA), a second subset of        filtered PCG outputs of the plurality of filtered PCG outputs to        form the M number of filtered ICA transforms; transforming, by        the at least one computer processor, utilizing the        Independent-Component-Analysis (ICA), a first portion of the        second subset of denoised filtered PCG outputs to form the M        number of denoised filtered ICA transforms; compiling, by the at        least one computer processor, a S number of a plurality of        detection heartbeat (DH) inputs, comprising: i) the M number of        filtered PCG outputs, ii) the M number of the denoised filtered        PCG outputs, iii) the M number of the filtered ICA transforms,        and iv) the M number of the denoised filtered ICA transforms;        detecting, by the at least one computer processor, beat        locations of beats in each of DH inputs; calculating, by the at        least one computer processor, a confidence score that describes        a probability that the beats in each DH input of the plurality        of DH inputs represent actual heartbeats and not a noise;        dividing, by the at least one computer processor, the plurality        of DH inputs into at least two groups: i) a first group of DH        inputs containing fetal heartbeats, ii) a second group of DH        inputs containing maternal heartbeats; selecting, by the at        least one computer processor, from the first group of DH inputs,        at least one particular fetal DH input that contains the fetal        heartbeat based on a first confidence score of the at least one        particular fetal DH input; and selecting, by the at least one        computer processor, from the second group of DH inputs, at least        one particular maternal DH input that contains the maternal        heartbeat, based on a second confidence score of the at least        one particular maternal DH input;    -   f) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval,            -   wherein the estimated fetal heart rate over the                particular time interval is calculated based on (1) cost                representing fetal heart probability mesh values at each                point of the estimated fetal heart rate over the                particular time interval; and (2) cost representing the                overall tortuosity of the estimated fetal heart rate                over the particular time interval.

The Garment According to Some Embodiments of the Present Invention

In one embodiment, the present invention provides a garment thatincludes:

-   -   a) at least one electrocardiogram sensor configured to contact        the skin of the abdomen of a pregnant human subject and detect        fetal and maternal cardiac electrical activity;    -   b) at least one acoustic sensor configured to contact the skin        of the abdomen of a pregnant human subject and detect fetal and        maternal cardiac electrical activity; and    -   c) a garment configured to position and contact the at least one        electrocardiogram sensor and the at least one acoustic sensor on        the abdomen of the pregnant human subject.

In one embodiment, the garment is further configured to include:

-   -   a) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving raw        Electrocardiogram (ECG) signals data from the at least one pair        of ECG sensors; wherein the at least one pair of ECG sensors is        positioned in on an abdomen of a pregnant human subject; wherein        the raw ECG signals data comprise data representative of a N        number of raw ECG signals (raw N-ECG signals data) which are        being acquired in real-time from the at least one pair of ECG        sensors; digital signal filtering the raw ECG signals data to        form filtered N-ECG signals data having filtered N-ECG signals;        detecting maternal heart peaks in each of the filtered N-ECG        signal in the filtered N-ECG signals data; subtracting, from        each of the filtered N-ECG signal of the filtered N-ECG signals        data, the maternal ECG signal, by utilizing at least one        non-linear subtraction procedure to obtain corrected ECG signals        data which comprise data representative of a N number of        corrected ECG signals (corrected N-ECG signals data), wherein        the at least one non-linear subtraction procedure comprises:        iteratively performing: i) dividing each filtered N-ECG signal        of N-ECG signals of the filtered N-ECG signals data into a        second plurality of ECG signal segments,) wherein each ECG        signal segment of the plurality of ECG signal segments        corresponds to a beat interval of a full heartbeat, and 2)        wherein each beat interval is automatically determined based, at        least in part on automatically detecting an onset value and an        offset value of such beat interval; ii) modifying each of the        plurality of filtered N-ECG signal segments to form a plurality        of modified filtered N-ECG signal segments, wherein the        modifying is performed using at least one inverse optimization        scheme based on a set of parameters, wherein values of the set        of parameters is determined based on: iteratively performing: 1)        defining a global template based on a standard heartbeat profile        of an adult human being; 2) setting a set of tentative values        for a local template for each filtered N-ECG signal segment;        and 3) utilizing at least one optimization scheme to determine        an adaptive template for each filtered N-ECG signal segment        based on the local template being matched to the global template        within a pre-determined similarity value; and iii) eliminating        the modified segments from each of the filtered N-ECG signals,        by subtracting the adaptive template from the filtered N-ECG        signal thereby generating each corrected ECG signal; extracting        raw fetal ECG signals data from the filtered N-ECG signals data        based on the corrected ECG signals data, wherein the raw fetal        ECG signals data comprises a N number of fetal ECG signals (raw        N-ECG fetal signals data); processing the raw N-ECG fetal        signals data to improve a signal-to-noise ratio of the N-ECG        fetal signals to form filtered N-ECG fetal signals data;        detecting fetal heart peaks in the filtered N-ECG fetal signals        data; calculating, based on detected fetal heart peaks, at least        one of: i) fetal heart rate, ii) fetal heart curve, iii)        beat-2-beat fetal heart rate, or iv) fetal heart rate        variability; and outputting a result of the calculating        operation;    -   b) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations: receiving, by at        least one computer processor executing specific programmable        instructions configured for the method, a plurality of        Phonocardiogram (PCG) signals data inputs from a plurality of        acoustic sensors; digital signal filtering, by the at least one        computer processor, utilizing a plurality of bandpass filters,        the plurality of PCG signals data inputs to form a plurality of        filtered PCG outputs, wherein the plurality of bandpass filters        comprises a L number of bandpass filters, wherein each bandpass        filter outputs a K number of filtered PCG outputs; wavelet        denoising, by the at least one computer processor, a first        subset of filtered PCG outputs of the plurality of filtered PCG        outputs to form a M number of denoised filtered PCG outputs,        wherein M is equal to L multiply by K; transforming, by the at        least one computer processor, utilizing an        Independent-Component-Analysis (ICA), a second subset of        filtered PCG outputs of the plurality of filtered PCG outputs to        form the M number of filtered ICA transforms; transforming, by        the at least one computer processor, utilizing the        Independent-Component-Analysis (ICA), a first portion of the        second subset of denoised filtered PCG outputs to form the M        number of denoised filtered ICA transforms; compiling, by the at        least one computer processor, a S number of a plurality of        detection heartbeat (DH) inputs, comprising: i) the M number of        filtered PCG outputs, ii) the M number of the denoised filtered        PCG outputs, iii) the M number of the filtered ICA transforms,        and iv) the M number of the denoised filtered ICA transforms;        detecting, by the at least one computer processor, beat        locations of beats in each of DH inputs; calculating, by the at        least one computer processor, a confidence score that describes        a probability that the beats in each DH input of the plurality        of DH inputs represent actual heartbeats and not a noise;        dividing, by the at least one computer processor, the plurality        of DH inputs into at least two groups: i) a first group of DH        inputs containing fetal heartbeats, ii) a second group of DH        inputs containing maternal heartbeats; selecting, by the at        least one computer processor, from the first group of DH inputs,        at least one particular fetal DH input that contains the fetal        heartbeat based on a first confidence score of the at least one        particular fetal DH input; and selecting, by the at least one        computer processor, from the second group of DH inputs, at least        one particular maternal DH input that contains the maternal        heartbeat, based on a second confidence score of the at least        one particular maternal DH input;    -   c) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval,            -   wherein the estimated fetal heart rate over the                particular time interval is calculated based on (1) cost                representing fetal heart probability mesh values at each                point of the estimated fetal heart rate over the                particular time interval; and (2) cost representing the                overall tortuosity of the estimated fetal heart rate                over the particular time interval.

In some embodiments, the system of the present invention can includes atleast one additional maternal or fetal cardiac sensing modality. Suchmodality can also replace the said electric or acoustic cardiac sensingmodality. This can be an active or passive sensing modality, selectedfrom the group consisting of: ultrasound, Doppler, ultrasound/Doppler(which may be described as high frequency acoustic signals with sensorsusing Doppler technology), PAT (which stands for Peripheral ArterialTone) optical sensor which observes maternal heart rate from skin colorchanges and FNIRS (which stands for functional near infra-redspectroscopy).

In some embodiments, the acoustic sensor can be a vibro-acoustic sensorwhich senses the acoustic signal of the closure of the valves and alsosenses the cardiac vibrations of the contractions of the heart muscle.

In some embodiments, the garment is configured to allow the system to beambulatory and operable by the pregnant human subject in any setting,such as, for example, at home. For example, referring to FIG. 2-FIG. 4,the garment (1) is incorporated with at least one ECG sensor (3), atleast one acoustic sensor (2) and a transmitter (4) for communicatingthe output of the one or more processors to a remote server via at leastone of low emission Bluetooth (Bluetooth Low Energy) for communicationof an output of the processor to a remote server.

In some embodiments, the garment is washable. In some embodiments,wiring necessary for the operation of the system is waterproof, andhidden.

In some embodiments, the garment is made from a non-irritating material.

In the embodiment shown in FIG. 2-FIG. 4, the at least one acousticsensor is removable. In alternate embodiments, the at least one acousticsensor is removable. In alternate embodiments, the transmitter isremovable.

In some embodiments, the garment further includes audio speakers.

In some embodiments, the garment further includes a power supply.

In some embodiments, the garment comprises the belt disclosed in U.S.Pat. No. 8,396,229 B2.

In some embodiments, the garment is manufactured from the materialsdisclosed in U.S. Pat. No. 8,396,229 B2.

In some embodiments, the garment is manufactured according to themethods disclosed in U.S. Pat. No. 8,396,229 B2.

ECG Sensors According to Some Embodiments of the Present Invention

In some embodiments, the present invention provides an ECG sensorconfigured to detect fetal electrocardiogram signals, comprising:

-   -   a) a cutaneous contact for sensing fetal electrocardiogram        signals from a pregnant human subject;    -   b) a connector in electrical contact with the cutaneous contact        for connection to a lead wire; and    -   c) a substructure for attachment to a human pregnant subject        -   wherein, the cutaneous contact is configured on the            substructure to allow a surface of the cutaneous contact to            be in electrical communication with the skin of the pregnant            human subject.

Without intending to be limited to any particular theory, in someembodiments, the three-dimensional shape of the ECG sensor affects theperformance. For example, a curved profile without sharp angles islikely to prevent abrupt changes in the electrical field generated bythe cutaneous contact, or flow of current from the cutaneous contact tothe lead wire.

FIG. 5 shows a circular ECG sensor according to some embodiments of thepresent invention. In the embodiment shown in FIG. 5, the ECG sensor, asshown along the section A-A, comprises an electrically conductive fabric(5) attached over an elastomeric dome shaped circular structure (6),which is, in turn, attached to a circular foam backing. The foam backingis attached to a printed circuit board (8), which has one electricalconnection (9) that outputs the sensed fetal electrocardiogram signals,and at least one electrical connection (10) that connects theelectrically conductive fabric to the printed circuit board (8).

In some embodiments, the printed circuit board is configured tointerface the cutaneous contact with the lead wire. Alternatively, insome embodiments, the printed circuit board is further configured toperform additional functions, such as, for example, signal filtering, orpre-amplification.

FIG. 6 shows an ECG sensor according to some embodiments of the presentinvention. In the embodiment shown, the ECG sensor consists of ateardrop-shaped electrically conductive fabric, with a flat portion thatterminates at one end with a connection to a printed circuit board, anda dome-shaped structure that forms a skin contact at the opposite end.In the embodiment shown, only the dome-shaped structure would be exposedand touch the skin of the pregnant human subject.

FIGS. 7 and 8 show alternate embodiments of ECG sensors according to thepresent invention comprising a planar cutaneous contact.

In the embodiment shown in FIG. 5, the elastomeric dome shaped circularstructure is configured to maximize contact between the cutaneouscontact and the skin of the pregnant human subject under a all possibleattachment angles.

In the embodiment shown in FIG. 5, the elastomeric dome shaped circularstructure is configured to generate a profile without sharp angles whichare likely to affect performance of the ECG sensor.

In some embodiments, the elastomeric dome shaped circular structure hasa diameter ranging from 20 to 50 mm. In some embodiments, theelastomeric dome shaped circular structure has a diameter of 20 mm. Insome embodiments, the elastomeric dome shaped circular structure has adiameter of 25 mm. In some embodiments, the elastomeric dome shapedcircular structure has a diameter of 30 mm. In some embodiments, theelastomeric dome shaped circular structure has a diameter of 35 mm. Insome embodiments, the elastomeric dome shaped circular structure has adiameter of 40 mm. In some embodiments, the elastomeric dome shapedcircular structure has a diameter of 45 mm. In some embodiments, theelastomeric dome shaped circular structure has a diameter of 50 mm.

In some embodiments, the elastomeric dome shaped circular structure hasan un-deformed height (i.e. a height before pressure is applied) rangingfrom 5 to 15 mm. In some embodiments, the elastomeric dome shapedcircular structure has an un-deformed height of 5 mm. In someembodiments, the elastomeric dome shaped circular structure has anun-deformed height of 10 mm. In some embodiments, the elastomeric domeshaped circular structure has an un-deformed height of 15 mm.

In some embodiments, the circular foam backing has a thickness rangingfrom 0.3 to 5 mm. In some embodiments, the circular foam backing has athickness of 0.3 mm. In some embodiments, the circular foam backing hasa thickness of 0.5 mm. In some embodiments, the circular foam backinghas a thickness of 1 mm. In some embodiments, the circular foam backinghas a thickness of 1.5 mm. In some embodiments, the circular foambacking has a thickness of 2 mm. In some embodiments, the circular foambacking has a thickness of 2.5 mm. In some embodiments, the circularfoam backing has a thickness of 3 mm. In some embodiments, the circularfoam backing has a thickness of 3.5 mm. In some embodiments, thecircular foam backing has a thickness of 4 mm. In some embodiments, thecircular foam backing has a thickness of 4.5 mm. In some embodiments,the circular foam backing has a thickness of 5 mm.

In the embodiment shown in FIG. 6, the elastomeric dome shaped circularstructure is configured to generate a profile without sharp angles whichare likely affect performance of the ECG sensor. In some embodiments,the elastomeric dome shaped circular structure has a diameter rangingfrom 15 to 38 mm. In some embodiments, the elastomeric dome shapedcircular structure has a diameter of 15 mm. In some embodiments, theelastomeric dome shaped circular structure has a diameter of 20 mm. Insome embodiments, the elastomeric dome shaped circular structure has adiameter of 25 mm. In some embodiments, the elastomeric dome shapedcircular structure has a diameter of 30 mm. In some embodiments, theelastomeric dome shaped circular structure has a diameter of 35 mm. Insome embodiments, the elastomeric dome shaped circular structure has adiameter of 38 mm.

In some embodiments, the elastomeric dome shaped circular structure hasan un-deformed height (i.e. a height before pressure is applied) rangingfrom 5 to 15 mm. In some embodiments, the elastomeric dome shapedcircular structure has an un-deformed height of 5 mm. In someembodiments, the elastomeric dome shaped circular structure has anun-deformed height of 10 mm. In some embodiments, the elastomeric domeshaped circular structure has an un-deformed height of 15 mm.

Without intending to be limited to any particular theory, the skin-ECGsensor impedance varies with the pressure at which the ECG sensorcontacts the skin of the pregnant human subject. In some embodiments,the skin-ECG sensor impedance decreases as the pressure at which the ECGsensor contacts the skin of the pregnant human subject increases.

In some embodiments, the elastomeric dome is configured to deform whenplaced on the abdomen of the pregnant human subject and pressure isapplied to the ECG sensor. In some embodiments, the elastomeric dome isconfigured to deform when placed on the abdomen of the pregnant humansubject and pressure applied to create a skin-ECG sensor impedancesuitable for sensing fetal electrocardiogram signals from a pregnanthuman subject.

In some embodiments, the deformation of the elastomeric dome increasesthe surface area of the cutaneous contact that contacts the skin of thepregnant human subject. In some embodiments, 100% of the surface area ofthe cutaneous contact contacts the skin of the pregnant human subject.In an alternate embodiment, 90% of the surface area of the cutaneouscontact contacts the skin of the pregnant human subject. In an alternateembodiment, 80% of the surface area of the cutaneous contact contactsthe skin of the pregnant human subject. In an alternate embodiment, 70%of the surface area of the cutaneous contact contacts the skin of thepregnant human subject. In an alternate embodiment, 60% of the surfacearea of the cutaneous contact contacts the skin of the pregnant humansubject. In an alternate embodiment, 50% of the surface area of thecutaneous contact contacts the skin of the pregnant human subject. In analternate embodiment, 40% of the surface area of the cutaneous contactcontacts the skin of the pregnant human subject. In an alternateembodiment, 30% of the surface area of the cutaneous contact contactsthe skin of the pregnant human subject. In an alternate embodiment, 20%of the surface area of the cutaneous contact contacts the skin of thepregnant human subject. In an alternate embodiment, 10% of the surfacearea of the cutaneous contact contacts the skin of the pregnant humansubject. In an alternate embodiment, 75% of the surface area of thecutaneous contact contacts the skin of the pregnant human subject.

In some embodiments, the pressure applied is equivalent to a massranging between 0.2 kg to 5 kg. In some embodiments, the pressureapplied is equivalent to a mass of 0.2 kg. In some embodiments, thepressure applied is equivalent to a mass of 0.2 kg. In some embodiments,the pressure applied is equivalent to a mass of 0.3 kg. In someembodiments, the pressure applied is equivalent to a mass of 0.4 kg. Insome embodiments, the pressure applied is equivalent to a mass of 0.5kg. In some embodiments, the pressure applied is equivalent to a mass of0.6 kg. In some embodiments, the pressure applied is equivalent to amass of 0.7 kg. In some embodiments, the pressure applied is equivalentto a mass of 0.8 kg. In some embodiments, the pressure applied isequivalent to a mass of 0.9 kg. In some embodiments, the pressureapplied is equivalent to a mass of 1 kg. In some embodiments, thepressure applied is equivalent to a mass of 1.5 kg. In some embodiments,the pressure applied is equivalent to a mass of 2 kg. In someembodiments, the pressure applied is equivalent to a mass of 2.5 kg. Insome embodiments, the pressure applied is equivalent to a mass of 3 kg.In some embodiments, the pressure applied is equivalent to a mass of 3.5kg. In some embodiments, the pressure applied is equivalent to a mass of4 kg. In some embodiments, the pressure applied is equivalent to a massof 4.5 kg. In some embodiments, the pressure applied is equivalent to amass of 5 kg.

In some embodiments, the pressure is applied using a garment, such as abelt.

In some embodiments, the suitable skin-ECG sensor impedance is between100 and 650 kΩ. In some embodiments, the suitable skin-ECG sensorimpedance is 602 kΩ. In some embodiments, the suitable skin-ECG sensorimpedance is less than 150 kΩ. In some embodiments, the suitableskin-ECG sensor impedance is 227 kΩ. In some embodiments, the suitableskin-ECG sensor impedance is 135 kΩ.

In some embodiments, the cutaneous contact is configured to haveskin-ECG sensor impedance of greater than 150 kΩ.

In some embodiments, the cutaneous contact is configured to haveskin-ECG sensor impedance of less than 150 kΩ.

In some embodiments, the cutaneous contact is configured to haveskin-ECG sensor impedance of between 5 to 150 kΩ. In some embodiments,the cutaneous contact is configured to have skin-ECG sensor impedance ofbetween 10 to 150 kΩ. In some embodiments, the cutaneous contact isconfigured to have skin-ECG sensor impedance of between 20 to 150 kΩ. Insome embodiments, the cutaneous contact is configured to have skin-ECGsensor impedance of between 30 to 150 kΩ. In some embodiments, thecutaneous contact is configured to have skin-ECG sensor impedance ofbetween 40 to 150 kΩ. In some embodiments, the cutaneous contact isconfigured to have skin-ECG sensor impedance of between 50 to 150 kΩ. Insome embodiments, the cutaneous contact is configured to have skin-ECGsensor impedance of between 60 to 150 kΩ. In some embodiments, thecutaneous contact is configured to have skin-ECG sensor impedance ofbetween 70 to 150 kΩ. In some embodiments, the cutaneous contact isconfigured to have skin-ECG sensor impedance of between 80 to 150 kΩ. Insome embodiments, the cutaneous contact is configured to have skin-ECGsensor impedance of between 90 to 150 kΩ. In some embodiments, thecutaneous contact is configured to have skin-ECG sensor impedance ofbetween 100 to 150 kΩ. In some embodiments, the cutaneous contact isconfigured to have skin-ECG sensor impedance of between 110 to 150 kΩ.In some embodiments, the cutaneous contact is configured to haveskin-ECG sensor impedance of between 120 to 150 kΩ. In some embodiments,the cutaneous contact is configured to have skin-ECG sensor impedance ofbetween 130 to 150 kΩ. In some embodiments, the cutaneous contact isconfigured to have skin-ECG sensor impedance of between 140 to 150 kΩ.

In some embodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of lessthan 150 kΩ.

In some embodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of between5 to 150 kΩ. In some embodiments, the cutaneous contact is attached toan elastomeric structure that is configured to deform when placed on theabdomen of the pregnant human subject to create a skin-ECG sensorimpedance of between 10 to 150 kΩ. In some embodiments, the cutaneouscontact is attached to an elastomeric structure that is configured todeform when placed on the abdomen of the pregnant human subject tocreate a skin-ECG sensor impedance of between 20 to 150 kΩ. In someembodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of between30 to 150 kΩ. In some embodiments, the cutaneous contact is attached toan elastomeric structure that is configured to deform when placed on theabdomen of the pregnant human subject to create a skin-ECG sensorimpedance of between 40 to 150 kΩ. In some embodiments, the cutaneouscontact is attached to an elastomeric structure that is configured todeform when placed on the abdomen of the pregnant human subject tocreate a skin-ECG sensor impedance of between 50 to 150 kΩ. In someembodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of between60 to 150 kΩ. In some embodiments, the cutaneous contact is attached toan elastomeric structure that is configured to deform when placed on theabdomen of the pregnant human subject to create a skin-ECG sensorimpedance of between 70 to 150 kΩ. In some embodiments, the cutaneouscontact is attached to an elastomeric structure that is configured todeform when placed on the abdomen of the pregnant human subject tocreate a skin-ECG sensor impedance of between 80 to 150 kΩ. In someembodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of between90 to 150 kΩ. In some embodiments, the cutaneous contact is attached toan elastomeric structure that is configured to deform when placed on theabdomen of the pregnant human subject to create a skin-ECG sensorimpedance of between 100 to 150 kΩ. In some embodiments, the cutaneouscontact is attached to an elastomeric structure that is configured todeform when placed on the abdomen of the pregnant human subject tocreate a skin-ECG sensor impedance of between 110 to 150 kΩ. In someembodiments, the cutaneous contact is attached to an elastomericstructure that is configured to deform when placed on the abdomen of thepregnant human subject to create a skin-ECG sensor impedance of between120 to 150 kΩ. In some embodiments, the cutaneous contact is attached toan elastomeric structure that is configured to deform when placed on theabdomen of the pregnant human subject to create a skin-ECG sensorimpedance of between 130 to 150 kΩ. In some embodiments, the cutaneouscontact is attached to an elastomeric structure that is configured todeform when placed on the abdomen of the pregnant human subject tocreate a skin-ECG sensor impedance of between 140 to 150 kΩ.

In some embodiments, the ECG sensor is configured to have a surfaceresistance suitable for sensing fetal electrocardiogram signals from apregnant human subject. In some embodiments, the cutaneous contact isconfigured to have a surface resistance of less than 1 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance between 0.01 and 1 Ω/square.

In some embodiments, the cutaneous contact is configured to have asurface resistance of 0.01 Ω/square. In some embodiments, the cutaneouscontact is configured to have a surface resistance of 0.02 Ω/square. Insome embodiments, the cutaneous contact is configured to have a surfaceresistance of 0.03 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.04 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.05 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.06 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.07 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.08 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.09 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.1 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.2 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.3 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.4 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.5 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.6 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.7 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 0.8 Ω/square. In some embodiments, the cutaneous contactis configured to have a surface resistance of 0.9 Ω/square. In someembodiments, the cutaneous contact is configured to have a surfaceresistance of 1 Ω/square.

In some embodiments, the ECG sensor is configured to have a capacitancesuitable for sensing fetal electrocardiogram signals from a pregnanthuman subject. In some embodiments, the capacitance is from 1 nF to 0.5μF. In some embodiments, the capacitance is 5 nF. In some embodiments,the capacitance is 10 nF. In some embodiments, the capacitance is 15 nF.In some embodiments, the capacitance is 20 nF. In some embodiments, thecapacitance is 25 nF. In some embodiments, the capacitance is 30 nF. Insome embodiments, the capacitance is 35 nF. In some embodiments, thecapacitance is 40 nF. In some embodiments, the capacitance is 45 nF. Insome embodiments, the capacitance is 50 nF. In some embodiments, thecapacitance is 60 nF. In some embodiments, the capacitance is 70 nF. Insome embodiments, the capacitance is 80 nF. In some embodiments, thecapacitance is 90 nF. In some embodiments, the capacitance is 80 nF. Insome embodiments, the capacitance is 0.1 μF. In some embodiments, thecapacitance is 80 nF. In some embodiments, the capacitance is 0.2 μF. Insome embodiments, the capacitance is 80 nF. In some embodiments, thecapacitance is 0.3 μF. In some embodiments, the capacitance is 80 nF. Insome embodiments, the capacitance is 0.4 μF. In some embodiments, thecapacitance is 80 nF. In some embodiments, the capacitance is 0.5 μF.

Without intending to be limited to any particular theory, thecapacitance of the ECG sensors increases as the surface area of thecutaneous contact that contacts the skin of the pregnant human subjectincreases. Additionally, without intending to be limited to anyparticular theory, the capacitance of the ECG sensors decreases as thepressure applied to the cutaneous contact increases.

In some embodiments, the ECG sensor is configured to detect a fetalelectrocardiogram signal having a signal to noise ratio between −20 dBand 50 dB. In some embodiments, the ECG sensor is configured to detect afetal electrocardiogram signal having a signal to noise ratio between 0dB and 50 dB. In some embodiments, the ECG sensor is configured todetect a fetal electrocardiogram signal having a signal to noise ratioless than 50 dB.

The Cutaneous Contact

In some embodiments, the cutaneous contact is an electrically conductivefabric. Electrically conductive fabrics can be made with conductivefibers, such as, for example, metal strands woven into the constructionof the fabric. Examples of electrically conductive fabrics suitable foruse in ECG sensors according to some embodiments of the presentinvention include, but are not limited to, the textile ECG sensorsdisclosed in Sensors, 12 16907-16919, 2012. Another example ofelectrically conductive fabrics suitable for use in ECG sensorsaccording to some embodiments of the present invention include, but arenot limited to, the textile ECG sensors disclosed in Sensors, 1411957-11992, 2014.

The electrically conductive fabric may be stretchable. Alternatively,the electrically conductive fabric may not be stretchable. Theelectrically conductive fabric may be capable of stretching up to 50%,alternatively, 40%, alternatively, 30%, alternatively 20%, alternatively20%, alternatively, 10%, alternatively, 9%, alternatively, 8%,alternatively, 7%, alternatively, 6%, alternatively, 5%, alternatively,4%, alternatively, 3%, alternatively, 2%, alternatively, 1%.

In some embodiments, the electrically conductive fabric is anisotropic.In some embodiments, the anisotropy is from 50% to 100%. As used herein,the term anisotropy refers to the difference in resistance of theelectrically conductive fabric measured in the main direction, comparedto the direction perpendicular to the main direction. As used herein,the term “main direction refers to the direction that the fabric waswoven. In some embodiments, the anisotropy of the electricallyconductive fabric is configured to have an anisotropy suitable forsensing fetal electrocardiogram signals from a pregnant human subject.In some embodiments, the anisotropy is 62%.

In some embodiments, the electrically conductive fabric is configured tobe oriented so the current recorded is the electrical activity that isgenerated by the fetal and/or maternal heart, and flows along the maindirection of the fabric to the lead wire. In some embodiments, theelectrically conductive fabric is configured to be oriented so thecurrent recorded is the electrical activity that is generated by thefetal and/or maternal heart, and flows along the direction of the fabrichaving the least resistance to the lead wire.

In some embodiments, the conductivity of one side of the electricallyconductive fabric is greater than the other. In some embodiments, theside of the electrically conductive fabric with the greater conductivityforms cutaneous contact.

In some embodiments, the electrically conductive fabric has a thicknessbetween 0.3 and 0.5 mm. In some embodiments, the thickness of theelectrically conductive fabric is 0.3 mm. In some embodiments, thethickness of the electrically conductive fabric is 0.4 mm. In someembodiments, the thickness of the electrically conductive fabric is 0.5mm.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename ORANGE IT. Anexample of this electrically conductive fabric is shown in FIG. 9.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename C+, sold byClothing+, St. Petersburg, Fla., USA. An example of this electricallyconductive fabric is shown in FIG. 10.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename SHAOXING17, soldby Shaoxing Yunjia Textile Product Co. Ltd., Zhejiang, China. An exampleof this electrically conductive fabric is shown in FIG. 11.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename SHAOXING27, soldby Shaoxing Yunjia Textile Product Co. Ltd., Zhejiang, China. An exampleof this electrically conductive fabric is shown in FIG. 12.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename SHIELDEXTECHNIK-TEX P130-B, sold by Shieldex Trading USA, Palmyra, N.Y., USA. Anexample of this electrically conductive fabric is shown in FIG. 13.

In some embodiments, the electrically conductive fabric is thesilver-based conductive fabric sold under the tradename SILVER30, soldby Shaoxing Yunjia Textile Product Co. Ltd., Zhejiang, China. An exampleof this electrically conductive fabric is shown in FIG. 14.

Acoustic Sensors According to Some Embodiments of the Present Invention

In one embodiment, the present invention provides an acoustic sensorconfigured to detect fetal cardiac activity signals, comprising:

-   -   a) a body formed of a solid integral mass having a circular        apron with two opposite major side walls,        -   a. one side wall being concave in configuration and the            other side wall having a rearwardly facing portion coaxially            formed thereon, the rearwardly facing portion defining an            attachment for a microphone;        -   b. an opening coaxially formed through the rearwardly facing            portion; and        -   c. an annular edge connecting the periphery of the side            walls;    -   b) a microphone, attached to the rearwardly facing portion,        -   wherein the microphone is configured to produce electrical            signals in response to acoustic signals transmitted from an            abdomen of a pregnant subject;    -   c) a flexible membrane covering the one side wall, wherein the        edge of the flexible membrane covers the annular edge of the one        side wall,        -   wherein the flexible membrane is configured to contact the            skin of the human pregnant subject,        -   wherein the flexible membrane is configured to transduce            transmitted acoustic signals to the body; and        -   wherein the body is configured to transmit the transduced            acoustic signals to the microphone;    -   d) an electrical conductor electrically connected to the        microphone; and    -   e) a connector in electrical contact with the electrical        conductor for connection to a lead wire.

In some embodiments, the acoustic sensor is configured to compensate forthe changes in sound propagation caused by the skin-air interface.Acoustic signals comprise sound waves or vibrations that propagate as amechanical wave of pressure and displacement, through a medium such asair, water, or the body. Without intending to be limited to anyparticular theory, the behavior of sound propagation can be affected bythe relationship between the density and pressure of the medium thoughwhich the sound wave propagates. Also, the behavior of sound propagationcan be affected by the motion of the medium though which the sound wavepropagates. Furthermore, the behavior of sound propagation can beaffected by the viscosity of the medium though which the sound wavepropagates.

Without intending to be limited to any particular theory, during anormal cardiac contraction cycle, the hear produces the followingsounds: S₁, which corresponds to the QRS complex of the cardiacelectrical activity observed during a normal cardiac contraction cycle,and is caused by the block of reverse blood flow due to closure of thetricuspid and mitral (bicuspid) valves, at the beginning of ventricularcontraction, or systole. S₂, which corresponds to the T wave of thecardiac electrical activity observed during a normal cardiac contractioncycle, and is caused by the closure of the aortic and pulmonary valves.

Referring to FIG. 22 and FIG. 23, the at least one acoustic sensorcomprises a body (11) formed of a solid integral mass having a circularapron with two opposite major side walls (12) and (13); one side wall(12) being concave in configuration and the other side wall (13) havinga rearwardly facing portion (14) coaxially formed thereon, therearwardly facing portion (14) defining an attachment for a microphone;an opening (15) coaxially formed through the rearwardly facing portion;and an annular edge (16) connecting the periphery of the side walls; amicrophone (17), attached to the rearwardly facing portion (14), whereinthe microphone (17) is configured to produce electrical signals inresponse to acoustic signals transmitted from an abdomen of a pregnantsubject; a flexible membrane (18) covering the one side wall, whereinthe edge of the flexible membrane (18) covers the annular edge (16) ofthe one side wall, wherein the flexible membrane (18) is configured tocontact the skin of the human pregnant subject, wherein the flexiblemembrane (18) is configured to transduce transmitted acoustic signals tothe body (11); and wherein the body (11) is configured to transmit thetransduced acoustic signals to the microphone (17); an electricalconductor electrically connected to the microphone; and a connector inelectrical contact with the electrical conductor for connection to alead wire.

In some embodiments, the microphone (17) is lockingly engaged on therearwardly facing portion (14) via friction. Alternative mechanisms tolockingly engage the microphone (17) on the rearwardly facing portion(14) include adhesive, screw threads, and the like.

Referring to FIG. 24, in some embodiments, the microphone is lockinglyengaged in a structure configured to isolate the microphone (17) fromacoustic signals not from the abdomen of the pregnant human subject, andposition the microphone (17) on the rearwardly facing portion (14).Examples of acoustic signals not from the abdomen of the pregnant humansubject include, but are not limited to sounds caused by movement of thepregnant human subject, or environmental noise.

In some embodiments, the microphone (17) is lockingly engaged in thestructure configured to isolate the microphone (17) from acousticsignals not from the abdomen of the pregnant human subject by friction.Alternative mechanisms to lockingly engage the microphone (17) in thestructure configured to isolate the microphone (17) from acousticsignals not from the abdomen of the pregnant human subject includeadhesive, screw threads, and the like.

In the embodiment shown in FIG. 23, the flexible membrane (18) has ahole (19). Alternatively, the flexible membrane (18) lacks a hole, andthe flexible membrane (18) forms an air-tight chamber, defined by thebody (11) and the flexible membrane (18).

In some embodiments, the present invention provides at least oneacoustic sensor configured to detect fetal cardiac activity signals,comprising:

-   -   a) a body (11) formed of a solid integral mass having a circular        apron with two opposite major side walls (12) and (13),        -   i. one side wall (12) being concave in configuration and the            other side wall (13) having a rearwardly facing portion (14)            coaxially formed thereon, the rearwardly facing portion            defining an attachment for a microphone;        -   ii. an opening (15) coaxially formed through the rearwardly            facing portion; and        -   iii. an annular edge (16) connecting the periphery of the            side walls (12) and (13);    -   b) a microphone (17), attached to the rearwardly facing portion        (14),        -   wherein the microphone (17) is configured to produce            electrical signals in response to acoustic signals            transmitted from an abdomen of a pregnant subject;    -   c) a flexible membrane (18) covering the one side wall, wherein        the edge of the flexible membrane (18) covers the annular edge        (16) of the one side wall,        -   wherein the flexible membrane (18) is configured to contact            the skin of the human pregnant subject,        -   wherein the flexible membrane (18) is configured to            transduce transmitted acoustic signals to the body (11); and        -   wherein the body (11) is configured to transmit the            transduced acoustic signals to the microphone (17);    -   d) an electrical conductor electrically connected to the        microphone; and    -   e) a connector in electrical contact with the electrical        conductor for connection to a lead wire.

In some embodiments, the at least one acoustic sensor is configured toreduce the acoustic impedance mismatch between skin and air, therebyimproving the performance of the at least one sensor.

Without intending to be limited to any particular theory, the body (11)is configured to detect fetal cardiac activity, but isolate themicrophone (17) from acoustic signals not from the abdomen of thepregnant human subject, and position the microphone (17) at the opening(15). Examples of acoustic signals not from the abdomen of the pregnanthuman subject include, but are not limited to sounds caused by movementof the pregnant human subject, or environmental noise. The sensitivityof the at least one acoustic sensor according to some embodiments of thepresent invention to fetal cardiac activity can be altered by varyingone or more parameters selected from the group consisting of: theflexibility of the flexible membrane, the diameter of the body, thesensitivity of the microphone, the material of the body, the size of thebody, the height of the cone defined by the concave configuration of theside wall (12), the material of the isolation structure, and thealgorithm used to extract fetal cardiac activity data from acousticsignals.

For example, by way of illustration, a larger acoustic sensor would beable collect more acoustic signals than a smaller one. By way of anotherillustration, an aluminum body would reflect sound waves moreefficiently (see, for example FIG. 25 than a plastic body.)

In some embodiments, the body (11) is circular, with an outer diameterconfigured to detect fetal cardiac activity. In some embodiments, thebody (11) is circular, with an outer diameter of 20 mm to 60 mm. In someembodiments, body (11) is circular, with an outer diameter of 60 mm. Insome embodiments, body (11) is circular, with an outer diameter of 50mm. In some embodiments, body (11) is circular, with an outer diameterof 43 mm. In some embodiments, body (11) is circular, with an outerdiameter of 40 mm. In some embodiments, body (11) is circular, with anouter diameter of 30 mm. In some embodiments, housing is body (11), withan outer diameter of 20 mm.

In some embodiments, the body (11) is non-circular in shape. Examples ofnon-circular shapes suitable for use according to some embodiments ofthe present invention include, but are not limited to, oval, square,rectangular, and the like.

In one embodiment, the distance between the two side walls (2) and (3)defines a thickness, wherein the thickness has a minimum value between0.3 mm to 5 mm. In some embodiments, the thickness is configured todetect fetal cardiac activity. In some embodiments, the thickness is 5mm. Alternatively, the thickness is 4 mm. Alternatively, the thicknessis 3 mm. Alternatively, the thickness is 2 mm. Alternatively, thethickness is 1 mm. Alternatively, the thickness is 0.9 mm.Alternatively, the thickness is 0.8 mm. Alternatively, the thickness is0.7 mm. Alternatively, the thickness is 0.6 mm. Alternatively, thethickness is 0.5 mm. Alternatively, the thickness is 0.4 mm.Alternatively, the thickness is 0.3 mm.

In some embodiments, the concave configuration of the side wall (12)defines a cone with a height from 1 mm to 15 mm. In some embodiments,the height of the cone is configured to detect fetal cardiac activity.In some embodiments, the height of the cone is 15 mm. Alternatively, insome embodiments, the height of the cone is 14 mm. Alternatively, insome embodiments, the height of the cone is 13 mm. Alternatively, insome embodiments, the height of the cone is 12 mm. Alternatively, insome embodiments, the height of the cone is 11 mm. Alternatively, insome embodiments, the height of the cone is 10 mm. Alternatively, insome embodiments, the height of the cone is 9 mm. Alternatively, in someembodiments, the height of the cone is 8 mm. Alternatively, in someembodiments, the height of the cone is 7 mm. Alternatively, in someembodiments, the height of the cone is 6 mm. Alternatively, in someembodiments, the height of the cone is 5 mm. Alternatively, in someembodiments, the height of the cone is 4 mm. Alternatively, in someembodiments, the height of the cone is 3 mm. Alternatively, in someembodiments, the height of the cone is 2 mm. Alternatively, in someembodiments, the height of the cone is 1 mm.

In some embodiments, the height of the cone is less than or equal to ¼the diameter of the base of the body (11).

In some embodiments, the body (11) is configured to have an acousticgain of 50 dB. Alternatively, in some embodiments, the body (11) isconfigured to have an acoustic gain of 40 dB. Alternatively, in someembodiments, the body (11) is configured to have an acoustic gain of 30dB. Alternatively, in some embodiments, the body (11) is configured tohave an acoustic gain of 20 dB. Alternatively, in some embodiments, thebody (11) is configured to have an acoustic gain of 10 dB.

In some embodiments, the acoustic gain is greater than the loss intransmission of the acoustic signal between skin and air as a result ofthe impedance mismatch.

In some embodiments, the loss in transmission is calculated using theequation:

$\tau =  {4\frac{Z_{air}*Z_{skin}}{( {Z_{air} + Z_{skin}} )^{2}}}arrow{\tau \cong {{- 29}\mspace{14mu} {dB}}} $

Where:

Z_(air) is the equivalent acoustic impedance of air

Z_(skin) is the equivalent acoustic impedance of skin

In some embodiments, the minimum acoustic gain G_(min) required tocompensate the loss τ is approximated using the equation:

$\mspace{20mu} {{\propto \frac{A_{base}}{A_{hole}}} = {( \frac{D_{base}}{D_{hole}} )^{2} = ( \frac{R_{base}}{r_{hole}} )^{2}}}$$G_{\min} = {{29\mspace{14mu} {dB}} = {{10\; \log \; 10( ( \frac{R_{base}}{r_{hole}} )^{2} )} = { {20\; \log \; 10( \frac{R_{base}}{r_{hole}} )}arrow{\frac{R_{base}}{r_{hole}} \geq 10^{\frac{29}{20}}}  = 28}}}$

Where:

A_(base) is the space of the base

A_(hole) is the space of the hole

R_(base) is the radius of the base

r_(hole) is the radius of the hole

D_(base) is the diameter of the base

D_(hole) is the diameter of the hole

In some embodiments, the hole (15) has a diameter from 2 mm to 5 mm. Insome embodiments, the hole (15) has a diameter of 5 mm. In someembodiments, the hole (15) has a diameter of 4 mm. In some embodiments,the hole (15) has a diameter of 3 mm. In some embodiments, the hole (15)has a diameter of 2 mm.

Referring to FIGS. 23 and 24, in some embodiments, microphone (17) islockingly engaged within a structure configured to isolate themicrophone (17) from acoustic signals not from the abdomen of thepregnant human subject, and wherein the structure is configured tolocate the microphone over the hole (15). In some embodiments, thestructure is has a spacer from 0.2 to 2 mm. In some embodiments, thespacer is 2 mm. In some embodiments, the spacer is 1.9 mm. In someembodiments, the spacer is 1.8 mm. In some embodiments, the spacer is1.7 mm. In some embodiments, the spacer is 1.6 mm. In some embodiments,the spacer is 1.5 mm. In some embodiments, the spacer is 1.4 mm. In someembodiments, the spacer is 1.3 mm. In some embodiments, the spacer is1.2 mm. In some embodiments, the spacer is 1.1 mm. In some embodiments,the spacer is 1.0 mm. In some embodiments, the spacer is 0.9 mm. In someembodiments, the spacer is 0.8 mm. In some embodiments, the spacer is0.7 mm. In some embodiments, the spacer is 0.6 mm. In some embodiments,the spacer is 0.5 mm. In some embodiments, the spacer is 0.4 mm. In someembodiments, the spacer is 0.3 mm. In some embodiments, the spacer is0.2 mm.

In some embodiments, the structure has a hole that transmits theacoustic signals from the body to the microphone (17). In someembodiments the structure is configured to lockingly engage themicrophone (17). In some embodiments, the microphone is lockinglyengaged within the structure via an adhesive.

In some embodiments, the structure has a height configured to isolatethe microphone (17) from acoustic signals not from the abdomen of thepregnant human subject. In some embodiments, the height is from 0.4 mmto 9 mm. In some embodiments, the height is 9 mm. In some embodiments,the height is 8 mm. In some embodiments, the height is 7 mm. In someembodiments, the height is 6 mm. In some embodiments, the height is 5.5mm. In some embodiments, the height is 5 mm. In some embodiments, theheight is 4.5 mm. In some embodiments, the height is 4 mm. In someembodiments, the height is 3.5 mm. In some embodiments, the height is 3mm. In some embodiments, the height is 2.5 mm. In some embodiments, theheight is 2 mm. In some embodiments, the height is 1.5 mm. In someembodiments, the height is 1 mm. In some embodiments, the height is 0.9mm. In some embodiments, the height is 0.8 mm. In some embodiments, theheight is 0.7 mm. In some embodiments, the height is 0.6 mm. In someembodiments, the height is 0.5 mm. In some embodiments, the height is0.4 mm.

In some embodiments, the structure comprises a circular portion. In someembodiments, the circular portion is configured to lockingly engage withthe rearwardly facing portion (14).

In some embodiments, the circular portion has an outer diameterconfigured to isolate the microphone (17) from acoustic signals not fromthe abdomen of the pregnant human subject. In some embodiments, theouter diameter is from 6 mm to 16 mm. In some embodiments, the outerdiameter is 16 mm. In some embodiments, the outer diameter is 15 mm. Insome embodiments, the outer diameter is 14 mm. In some embodiments, theouter diameter is 13 mm. In some embodiments, the outer diameter is 12mm. In some embodiments, the outer diameter is 11 mm. In someembodiments, the outer diameter is 10 mm. In some embodiments, the outerdiameter is 9 mm. In some embodiments, the outer diameter is 8 mm. Insome embodiments, the outer diameter is 7.1 mm. In some embodiments, theouter diameter is 7 mm. In some embodiments, the outer diameter is 6 mm.

In some embodiments the circular portion has an inner diameterconfigured to house the microphone (17) and isolate the microphone fromacoustic signals not from the abdomen of the pregnant human subject. Insome embodiments, the inner diameter is from 4 mm to 8 mm. In someembodiments, the inner diameter is 8 mm. In some embodiments, the innerdiameter is 7 mm. In some embodiments, the inner diameter is 6 mm. Insome embodiments, the inner diameter is 5 mm. In some embodiments, theinner diameter is 4 mm.

In some embodiments, the body (11) is made from a material configured todetect fetal cardiac activity. In some embodiments, the body (11) ismade from aluminum. In alternate embodiments, the body (11) is made frombrass. In alternate embodiments, the body (11) is made from stainlesssteel. In alternate embodiments, the body (11) is made from plastic. Insome embodiments, the plastic is nylon.

In some embodiments, the structure is made from a material configured toisolate the microphone (17) from acoustic signals not from the abdomenof the pregnant human subject. In some embodiments, the structure ismade from Polyurethane 60 shore. In some embodiments, the structure is aresin. Examples of materials suitable for forming the structure include,but are not limited to rubber, Silicone, TPE, TPU, and the like. In someembodiments the Polyurethane's elasticity is from 20 to 80 shore.

In some embodiments, the flexible membrane (18) is attached to the body(11) by adhesive. Alternatively, in some embodiments, the flexiblemembrane (18) is attached to the body (11) by crimping a portion of theflexible membrane between the body (11) and a housing, by vacuumforming, followed by snapping.

As used herein, the term “flexible” refers to the property of a membraneto deform, both to conform to the skin of the pregnant human subject,but also to transduce acoustic signals with sufficient fidelity to themicrophone.

In some embodiments, the size of the flexible membrane (18), thematerial comprising the flexible membrane (18), the thickness of theflexible membrane (18), or any combination thereof can alter the abilityof the flexible membrane (18) to contact the skin of a human pregnantsubject and to transduce the acoustic signals. In some embodiments, theflexible membrane (18) is configured to contact the skin of a humanpregnant subject and to transduce the acoustic signals. In someembodiments, the flexible membrane (18) is further configured tocomprise a hole (19).

In some embodiments, the thickness of the flexible membrane (18) is from0.2 mm to 0.6 mm. In some embodiments, the thickness of the flexiblemembrane (18) is 0.6 mm. In some embodiments, the thickness of theflexible membrane (18) is 0.5 mm. In some embodiments, the thickness ofthe flexible membrane (18) is 0.4 mm. In some embodiments, the thicknessof the flexible membrane (18) is 0.3 mm. In some embodiments, thethickness of the flexible membrane (18) is 0.2 mm.

In some embodiments, the density of the flexible membrane (18) is 900kg/m³ to 1900 kg/m3. In some embodiments, the density of the flexiblemembrane (18) is 1900 kg/m³. In some embodiments, the density of theflexible membrane (18) is 1800 kg/m³. In some embodiments, the densityof the flexible membrane (18) is 1700 kg/m³. In some embodiments, thedensity of the flexible membrane (18) is 1600 kg/m³. In someembodiments, the density of the flexible membrane (18) is 1500 kg/m³. Insome embodiments, the density of the flexible membrane (18) is 1400kg/m³. In some embodiments, the density of the flexible membrane (18) is1300 kg/m³. In some embodiments, the density of the flexible membrane(18) is 1200 kg/m³. In some embodiments, the density of the flexiblemembrane (18) is 1100 kg/m³. In some embodiments, the density of theflexible membrane (18) is 1000 kg/m³. In some embodiments, the densityof the flexible membrane (18) is 900 kg/m³.

In some embodiments, the flexible membrane (18) is circular, and has adiameter equal to the body. In some embodiments, the flexible membranehas same outer perimeter as the body (11).

In some embodiments, the flexible membrane (18) is circular, and has adiameter from 20 mm to 50 mm. In some embodiments, the flexible membrane(18) is circular, and has a diameter of 50 mm. In some embodiments, theflexible membrane (18) is circular, and has a diameter of 44 mm. In someembodiments, the flexible membrane (18) is circular, and has a diameterof 40 mm. In some embodiments, the flexible membrane (18) is circular,and has a diameter of 38 mm. In some embodiments, the flexible membrane(18) is circular, and has a diameter of 36 mm. In some embodiments, theflexible membrane (18) is circular, and has a diameter of 34 mm. In someembodiments, the flexible membrane (18) is circular, and has a diameterof 30 mm. In some embodiments, the flexible membrane (18) is circular,and has a diameter of 26 mm. In some embodiments, the flexible membrane(18) is circular, and has a diameter of 20 mm.

In some embodiments, the hole (19) has a diameter ranging from 0.4 mm to1.2 mm. In some embodiments, the hole (19) has a diameter of 1 mm. Insome embodiments, the hole (19) has a diameter of 0.8 mm. In someembodiments, the hole (19) has a diameter of 0.6 mm. In someembodiments, the hole (19) has a diameter of 0.4 mm. In someembodiments, the hole (19) is absent.

In some embodiments, the flexible membrane comprises PVC. In someembodiments the flexible membrane comprises Polyester, Polycarbonate. Insome embodiments, the flexible membrane comprises a Phenoxy resin. Insome embodiments, the flexible membrane comprises BoPET, such as, forexample, the membrane sold under the trade name MYLAR®. In someembodiments, the flexible membrane comprises BoPET, such as, forexample, the membrane sold under the trade name HOSTAPHAN®.

In some embodiments, the flexible membrane is the flexible membranedisclosed in U.S. Pat. No. 3,276,536.

In some embodiments, the microphone (17) is configured to detect fetalcardiac activity.

In some embodiments, the microphone (17) is a free air microphone.Alternatively, in some embodiments, the microphone (17) is a contactmicrophone. Alternatively, in some embodiments, the microphone (17) is ahybrid free air and contact microphone.

In some embodiments, the microphone (17) is configured to detect sub-ELF(extremely low frequency) signals. In some embodiments, the microphoneis configured to have a flat response in the 5-150 Hz region.

In some embodiments, the microphone (17) is an electrostaticcapacitor-based microphone. In some embodiments, the electrostaticcapacitor-based microphone is a foil, or diaphragm type electrostaticcapacitor-based microphone. In some embodiments, the electrostaticcapacitor-based microphone is a back electret type electrostaticcapacitor-based microphone. In some embodiments, the electrostaticcapacitor-based microphone is a front electret type electrostaticcapacitor-based microphone.

Referring to FIG. 2 to FIG. 4, an example of a garment according to someembodiments of the present invention is shown. In the embodiment shown,4 acoustic sensors (2) are incorporated into a belt (1), wherein thebelt, when worn, positions the acoustic sensors on the abdomen of thepregnant mother, such that the acoustic sensors contact the skin of theabdomen of the pregnant mother, and the acoustic sensors are positionedin a circumferential arrangement around the uterus. In the embodimentsshown, the belt also contains additional sensors (4) and a transmitter(3).

In some embodiments, the additional sensors are ECG sensors.

For example, as shown in FIG. 29, the exemplary inventive system of thepresent invention utilizes a set of four acoustic sensors (M1-M4) atrespective exemplary positions. In some embodiments, the positioning ofacoustic sensors can varies based, at least in part, on, for example,shape of mother's stomach, the stage of the pregnancy, physiologicalcharacteristics of the pregnant human subject and/or fetus(es), previousacoustic and/or other types of cardio recordings (e.g.,Electrocardiogram (ECG) signal recordings and analysis, etc.), and othersimilarly suitable data.

An alternate positioning of the acoustic sensors is shown in FIG. 27.

In some embodiments, the acoustic sensors of the present inventionrecord the internal sound produced inside the pregnant human subjectwith added noise from the environment. As detailed below, from theserecordings the heartbeat sound of the fetus(es) and/or the pregnanthuman subject are extracted and the heart rate of each subject iscalculated.

In some embodiments, the level of detection by each acoustic sensor isindependent of the other acoustic sensors (e.g, in FIG. 29, on otherthree acoustic sensors). Referring to FIG. 29, in some embodiments, itis determined that, typically, the fetal PCG signals are detected by theacoustic sensors in locations M3 and/or M4, while the maternal PCGsignals are detected by the acoustic sensors in locations M1 and/or M2.In some embodiments, the maternal PCG signals can be detected by allfour sensors (M1-M4) and have to be cleaned in order to detect the fetalheartbeats. In some embodiments, as detailed below, the cleaning processis performed using at least one Independent component analysis (ICA)algorithm of the present invention. For instance, in some embodiments,the inventive system of the present invention assumes that theinterfering noises are audio sources which are not the fetal origin thatthus are statistically independent from the fetal heart sounds. Anexample of maternal acoustic signals detected using an acoustic sensoraccording to some embodiments of the present invention is shown in FIG.28.

ECG Signals Data Processing According to Some Embodiments of the PresentInvention

In some embodiments, ECG signals data is processed according to themethods described in U.S. patent application Ser. No. 14/921,489.

In some embodiments, the arrangement of the ECG sensors provides asystem for recording, detecting and analyzing fetal cardiac electricalactivity data regardless of sensor position, fetal orientation, fetalmovement, or gestational age. In some embodiments, the ECG sensors areattached, or positioned, on the abdomen of the pregnant human subject inthe configuration shown in FIG. 29. In some embodiments, the ECG sensorsare divided into channels comprising a pair of ECG sensors, and cardiacelectrical activity data is recorded simultaneously from the channels.In some embodiments, the channels output the acquired signal data,corresponding to the recorded cardiac electrical activity data.

In some embodiments, at least one ECG sensor pair is used to obtain theacquired signal data. In some embodiments, the number of acquired signaldata is referred to as “N”. In some embodiments, the ability of thesystem to detect fetal cardiac electrical activity data is increased byincreasing the value of N. For example, by way of a non-limitingillustration, in some embodiments, the channels are specified asfollows:

1. B1-B3

2. B1-B2

3. B2-B3

4. A1-A4

5. A2-A3

6. A2-A4

In some embodiments, the signal data corresponding to fetal cardiacelectrical activity data are extracted from the acquired signal data.

As used herein, in some embodiments, the term “N-ECG signals” refers toECG signals data received from more than one, or N-ECG sensors.

In some embodiments the system of the present invention receives ECGsignals from 1 electrode, comprising N-ECG patterns corresponding to Nheart beats.

Fetal cardiac activity elicits a semi-periodic electrical signal,typically from about 0.1 to 100 Hz. Frequently, signals corresponding tofetal cardiac activity are contaminated with other electrical signals,including maternal cardiac electrical activity. The signal elicited bymaternal cardiac activity can be 10-fold greater than the fetal signalcorresponding to fetal cardiac activity. See, for example, FIG. 31,showing an exemplary recording of cardiac electrical activity data,showing both maternal and fetal electrical activity combined.

In some embodiments, the fetal cardiac electrical activity data areextracted from the acquired signal data by a method comprising:

-   -   a) obtaining raw N-ECG signals data by recording electrical        activity from the abdomen of a pregnant human subject carrying a        fetus using at least one ECG sensor pair;    -   b) applying a set of linear and no-linear mathematical        transformations to the raw N-ECG signals data, thereby obtaining        transformed/corrected N-ECG signals data; and    -   c) finding features in the transformed/corrected N-ECG signals        data that correlates to fetal or maternal cardiac electrical        activity.

The term “transformations” as used herein refers to linear or non-linearmathematical transformations, which may include, inter alia, digitalfiltering, mathematical linear or non-linear decomposition, mathematicaloptimization.

In some embodiments, the fetal cardiac electrical activity data areextracted from the acquired signal data using the algorithm shown inFIG. 30. Using the algorithm shown in FIG. 30, the recorded signal dataare pre-processed to remove noise (“Clean up signals”), then the peaksof the maternal cardiac electrical activity are detected (DetectMaternal Peaks”), the maternal cardiac activity signal data is removed(“Remove Maternal Signals”), then the resulting data is processed toremove noise (“Clean up signals”), then the peaks of the fetal cardiacelectrical activity are detected (Detect Fetal Peaks”), to detect fetalcardiac activity. The detected fetal activity data is then subsequentlyanalyzed to calculate at least one of the parameters selected from thegroup consisting of: beat-to-beat fetal heart rate, fetal ECG, averagefetal heart rate, and the variability of fetal heart rate.

In some embodiments, the present invention provides acomputer-implemented method, comprising: receiving, by at least onecomputer processor executing specific programmable instructionsconfigured for the method, raw Electrocardiogram (ECG) signals data fromat least one pair of ECG sensors; wherein the at least one pair of ECGsensors are positioned in on an abdomen of a pregnant human subject;wherein the raw ECG signals data comprise data representative of a Nnumber of raw ECG signals data (raw N-ECG signals data) which are beingacquired in real-time from the at least one pair of ECG sensors; digitalsignal filtering, by the at least one computer processor, the raw N-ECGsignals data to form filtered N-ECG signals data having filtered N-ECGsignals; detecting, by the at least one computer processor, maternalheart peaks in each filtered ECG signal in the filtered N-ECG signalsdata; subtracting, by the at least one computer processor, from each ofN-ECG signal of the filtered N-ECG signals data, maternal ECG signal, byutilizing at least one non-linear subtraction procedure to obtaincorrected ECG signals data which comprise data representative of a Nnumber of corrected ECG signals, wherein the at least one non-linearsubtraction procedure comprises: iteratively performing: i)automatically dividing each ECG signal of N-ECG signals of the filteredN-ECG signals data into a plurality of ECG signal segments, 1) whereineach ECG signal segment of the plurality of ECG signal segmentscorresponds to a beat interval of a full heartbeat, and 2) wherein eachbeat interval is automatically determined based, at least in part onautomatically detecting an onset value and an offset value of such beatinterval; ii) automatically modifying each of the second plurality offiltered N-ECG signal segments to form a plurality of modified filteredN-ECG signal segments, wherein the modifying is performed using at leastone inverse optimization scheme based on a set of parameters, whereinvalues of the set of parameters is determined based on: iterativelyperforming: 1) defining a global template based on a standard heartbeatprofile of an adult human being, 2) setting a set of tentative valuesfor a local template for each filtered N-ECG signal segment, 3)utilizing at least one optimization scheme to determine an adaptivetemplate for each filtered N-ECG signal segment based on the localtemplate being matched to the global template within a pre-determinedsimilarity value; and iii) automatically eliminating the modifiedsegments from each of the filtered N-ECG signals, by subtracting theadaptive template from the filtered N-ECG signal thereby generating eachcorrected ECG signal; extracting, by the at least one computerprocessor, raw fetal ECG signals data from the filtered N-ECG signalsdata based on the corrected ECG signals data, wherein the raw fetal ECGsignals data comprises a N number of fetal ECG signals (raw N-ECG fetalsignals data); processing, by the at least one computer processor, theraw N-ECG fetal signals data to improve a signal-to-noise ratio of theraw N-ECG fetal signals data to form filtered N-ECG fetal signals data;and detecting, by the at least one computer processor, fetal heart peaksin the filtered N-ECG fetal signals data; and calculating, by the atleast one computer processor, based on detected fetal heart peaks, atleast one of: i) fetal heart rate, ii) fetal heart curve, iii)beat-2-beat fetal heart rate, or iv) fetal heart rate variability; andoutputting, by the at least one computer processor, a result of thecalculating step.

Pre-Processing of the Acquired Signal Data

In some embodiments, the raw N-ECG signals data are preprocessed toremove noise, to generate filtered N-ECG signals data. In someembodiments, the preprocessing comprises applying a digital signalingfilter selected from the group consisting of: a baseline wander filter,a power line frequency filter, and a high frequency filter.

In some embodiments, the baseline wander filter is intended to removelow frequency compartments of the recorded signals and enhance fast timevarying parts of the signal.

In some embodiments, the baseline wander filter is a moving averagefilter with constant weights. In some embodiments, the moving averagefilter with a length of 501 milliseconds is used.

In some embodiments, the baseline wander filter is a moving medianfilter.

In some embodiments, the powerline frequency filter is intended toremove the power line interference that is picked up by the ECG sensorpairs. In some embodiments, the cut-off frequency of powerline frequencyfilter is set to the powerline frequency of the geographical area wherethe system of the present invention is used. For example, if the systemis used in Europe, in some embodiments, the cut-off frequency is 49.5 to50.5 Hz. If the system is used in the USA, in some embodiments, thecut-off frequency is 59.5 to 60.5 Hz.

In some embodiments, the cut-off frequency of the powerline frequencyfilter is ±10 Hz of the powerline frequency of the geographical areawhere the system of the present invention is used. In some embodiments,the cut-off frequency of the powerline frequency filter is ±5 Hz of thepowerline frequency of the geographical area where the system of thepresent invention is used.

In some embodiments, a Butterworth type band-step filter with a cut-offfrequency of 49.5 to 50.5 Hz whose order is 10th order, 5 sections isused. In some embodiments, a band-stop digital filter is used toattenuate the frequency components of the power line interference. Insome embodiments, a digital adaptive filter is used to automaticallydetermine the exact power line frequency before applying the band-stopfilter.

In some embodiments, the high frequency filter is intended to removevery high frequency compartments from the acquired signals. In someembodiments, the high frequency filter is a digital low pass filter. Insome embodiments, the digital low pass filter is used to attenuates thehigh frequency compartments of the acquired signals. In someembodiments, the digital low pass filter is a Chebyshev type I low passfilter.

In some embodiments, the cutoff frequency of the low pass filter is setto 70 cycles/second. In some embodiments, the baseline ripple is 0.12decibels. In some embodiments, the order is 12th order, 6 sections.

In some embodiments, the high frequency filter is a smoothing filter. Insome embodiments, the smoothing filter is used to attenuate the highfrequency compartments of the raw N-ECG signals data.

In some embodiments, the high frequency filter is an edge preservingfilter. In some embodiments, the edge preserving filter is used toremove high frequency noise from the raw N-ECG signals data whilepreserving valuable information regarding the fetal and maternal ECGsignals contained within the raw N-ECG signals data. In someembodiments, the edge preserving filter is an adaptive mean filter. Insome embodiments, the edge preserving filter is an adaptive medianfilter.

In some embodiments, an additional transformation is applied to thefiltered N-ECG signals data. In some embodiments, a digital adaptiveinverse-median filter is applied to the filtered N-ECG signals data toenhance the maternal ECG peaks.

The term “maternal ECG peaks” as used herein refers any of the P, Q, R,S or T waves of the electrical activity during a cardiac contractioncycle. FIG. 32 shows a depiction of the electrical activity during acardiac contraction cycle.

In some embodiments, the additional transformation comprises applying anadaptive median filter to the N filtered N-ECG signals data, andsubtracting the result filtered N-ECG signals data.

In some embodiments, the length of the adaptive median filter isselected to be constant. In some embodiments, the length is set to 100samples.

In some embodiments, the length of the adaptive median filter is adapteddepending on the local characteristics of the maternal ECG peaks.

As used herein, the term “local” refers to the signal recorded from asensor positioned in at a particular location on the abdomen of thepregnant mother.

In some embodiments, the local characteristics are the duration of theQRS segment of the maternal electrical activity during a cardiaccontraction cycle.

In some embodiments, the local characteristics are the duration of theST segment of the maternal electrical activity during a cardiaccontraction cycle.

In some embodiments, the local characteristics are the duration of thePR segment of the maternal electrical activity during a cardiaccontraction cycle.

In some embodiments, the additional transformation comprises adecomposition to filtered N-ECG signals data to improve the signal tonoise ratio. In some embodiments, the decomposition is a singular valuedecomposition (SVD). In some embodiments, the decomposition is principlecomponent analysis (PCA). In some embodiments, the decomposition isindependent component analysis (ICA). In some embodiments, thedecomposition is wavelet decomposition (CWT).

In some embodiments, an additional high pass filter is applied to thedecomposed filtered N-ECG signals data. In some embodiments, the highpass filter is 5^(th) order at 1 Hz. In some embodiments, the decomposedN filtered signal data is examined by preliminary and simple peakdetector. In some embodiments, the relative energy of the peaks iscalculated (relative to the overall energy of the signal). In someembodiments, the decomposed filtered N-ECG signals data is given aquality score depending on this measure, decomposed filtered N-ECGsignals data with a quality score of less than a threshold are excludedand the signals are examined for missing data and NaNs (characters thatare non-numerical).

In some embodiments, the quality score is assigned by calculating therelation (energy of peaks)/(Total energy of the filtered N-ECG signal).In some embodiments, the energy of the temporary peaks is calculated bycalculating the root mean square of the detected peaks. In someembodiments, the energy of the signal is calculated by calculating theroot mean square of the filtered N-ECG signals.

In some embodiments, the threshold is any value from 0.3 to 0.9. In someembodiments, the threshold is 0.8.

Detection of the Portion of the Acquired Signals Corresponding toMaternal Cardiac Activity from the Filtered N-ECG Signals Data andElimination of the Signals Corresponding to Maternal Cardiac Activityfrom the Filtered N-ECG Signals Data

In some embodiments, maternal heart peaks in each filtered N-ECG signalin the filtered N-ECG signals data are detected and subtracted from eachof N-ECG signal of the filtered N-ECG signals data, by utilizing atleast one non-linear subtraction procedure to obtain corrected N-ECGsignals data which comprise data representative of a N number ofcorrected ECG signals, wherein the at least one non-linear subtractionprocedure comprises: iteratively performing: i) automatically dividingeach ECG signal of N-ECG signals of the filtered N-ECG signals data intoa plurality of ECG signal segments, 1) wherein each ECG signal segmentof the plurality of ECG signal segments corresponds to a beat intervalof a full heartbeat, and 2) wherein each beat interval is automaticallydetermined based, at least in part on automatically detecting an onsetvalue and an offset value of such beat interval; ii) automaticallymodifying each of the second plurality of filtered N-ECG signal segmentsto form a plurality of modified filtered N-ECG signal segments, whereinthe modifying is performed using at least one inverse optimizationscheme based on a set of parameters, wherein values of the set ofparameters is determined based on: iteratively performing: 1) defining aglobal template based on a standard heartbeat profile of an adult humanbeing, 2) setting a set of tentative values for a local template foreach filtered N-ECG signal segment, 3) utilizing at least oneoptimization scheme to determine an adaptive template for each filteredN-ECG signal segment based on the local template being matched to theglobal template within a pre-determined similarity value; and iii)automatically eliminating the modified segments from each of thefiltered N-ECG signals, by subtracting the adaptive template from thefiltered N-ECG signal thereby generating each corrected ECG signal.

Examples of adaptive templates according to some embodiments of thepresent invention are shown in FIGS. 32, 35 and 36.

In some embodiments, detecting of the maternal heart peaks in eachfiltered ECG signal in the filtered N-ECG signals data is performedbased at least on: i) dividing each filtered ECG signal into a firstplurality of ECG signal segments; ii) normalizing a filtered ECG signalin each ECG signal segment; iii) calculating a first derivative of thefiltered ECG signal in each ECG signal segment; iv) finding localmaternal heart peaks in each ECG signal segment based on determining azero-crossing of the first derivative; and v) excluding the localmaternal heart peaks having at least one of: 1) an absolute value whichis less than a pre-determined local peak absolute threshold value; or 2)a distance between the local peaks is less than a pre-determined localpeak distance threshold value.

In some embodiments, the pre-determined similarity value is based on anEuclidian distance, wherein the set of parameters is a local minimasolution to a non-linear least squares problem solved by at least oneof: 1) minimizing a cost function taken as the Euclidian distance; 2)utilizing a Gauss-Newton algorithm; 3) utilizing a Steepest-Descent(Gradient-Descent) algorithm; or 4) utilizing a Levenberg-Marquardtalgorithm.

In some embodiments, the length of each segment is set at 10 seconds.

In some embodiments, the length of each segment is selectedautomatically depending on the length of the recording.

In some embodiments, the signal data in each segment is normalized bythe absolute maximum value of the signal data. In some embodiments, thesignal data in each segment is normalized by the absolute non-zerominimum value of the signal data.

In some embodiments, a first order forward derivative is used. In someembodiments, a first order central derivative is used.

In some embodiments, the threshold is selected to be a constant value of0.3.

In some embodiments, the threshold is selected depending on the localcharacteristics of the signal data. In some embodiments, the localcharacteristic of the signal is the median value of the signal data orany multiplication of this value. In some embodiments, the localcharacteristic of the signal is the mean value of the signal data or anymultiplication of this value.

In some embodiments, the threshold on the distance is selected to be 100samples.

In some embodiments, the local characteristics of the signal can be themaximum predicted RR internal or any multiple of this value.

In some embodiments, a “peaks array” is generated from the filteredN-ECG signals data. In some embodiments, the peak array comprises thenumber of detected peaks for each of the segments of the filtered N-ECGsignals data.

In some embodiments, clustering is performed on the peaks array. In someembodiments, k-means clustering is used to group the peaks into a numberof clusters. In some embodiments, k-medoids clustering is used to groupthe peaks into a number of clusters.

In some embodiments, the number of clusters for the clustering is set tobe three. In some embodiments, the number of clusters for the clusteringis selected automatically depending on the characteristics of theprocessed N filtered signal data.

In some embodiments, the clustering is used to exclude outliers. In someembodiments, outliers are peaks that have anomalous characteristics.

In some embodiments, the characteristic is the distance between a peakand its neighboring peaks. In some embodiments, the characteristic isthe amplitude of the peak.

In some embodiments, a new peak array is constructed after the exclusionof the anomalous peaks.

In some embodiments, the new peak array is further analyzed and thepeaks are scored depending on the signal to noise ratio of the filteredN-ECG signals data.

In some embodiments, the signal to noise ratio score is calculated bycalculating the relative energy of the QRS complexes from the overallenergy of the processed N filtered signal data.

In some embodiments, the detected peaks for each of the filtered N-ECGsignals data are fused for a more robust detection. In some embodiments,the fusion of the detected peaks is done using the scores given for eachof the peaks of the filtered N-ECG signals data.

In some embodiments, a global array of peaks is defined using the fusedpeaks.

In some embodiments, the peaks of each of the filtered N-ECG signalsdata is redetected and the positions are refined using the global peaksarray. In some embodiments, the global peak array is constructed basedon the best lead with corrections made using the peaks from the otherleads and the global peaks array is examined using physiologicalmeasures, such as, for example, RR intervals, HR, HRV).

In some embodiments, after the peaks have been defined, the filteredN-ECG signals data is further transformed to eliminate the signalscorresponding to maternal cardiac activity. In some embodiments, thetransformations/corrections include applying non-linear subtraction tothe filtered N-ECG signals data. In some embodiments, the remaining datacomprises signal data corresponding to fetal cardiac activity and noise.

In some embodiments of the invention, the non-linear subtractionprocedure is applied separately for each one of the processed N filteredsignal data. In some embodiments, the non-linear subtraction procedureis applied to all of the processed N filtered signal data in series, inany order. In some embodiments, the non-linear subtraction procedure isapplied to all of the processed N filtered signal data simultaneously.

In some embodiments, the non-linear subtraction comprises: dividing theprocessed N filtered signal data into a large number of segments;modifying each of the segments, separately or jointly, using an inverseoptimization scheme; and eliminating the modified segments from theoriginal processed N filtered signal data, thereby obtaining N raw fetalsignal data.

The term ‘segmentation’, as used herein, refers to the division of theprocessed N filtered signal data into any given number of segments.

In some embodiments, the number of segments is set to be a function ofthe number of the detected maternal peaks. In some embodiments, thefunction is the identity function so that the number of segments equalsthe number of the detected maternal peaks, in which case, each segmentis a full heartbeat.

In some embodiments, a beat interval is defined. The term ‘beatinterval’ used hereinafter refers the time interval of a single maternalheart beat.

In some embodiments, the beat interval is taken to be constant and isdefined as 500 milliseconds before the R-peak position and 500milliseconds after the R-peak position.

In some embodiments, the beat interval is detected automatically bydetecting the beat onset and beat offset of each beat. Thus, the beatinterval depends on the local heart rate value and a more accuratesegmentation of the ECG signal is achieved.

In some embodiments, the beat interval onset is defined as the onset(i.e. starting point in time) of the P-wave.

In some embodiments, the beat interval offset is defined as the offset(i.e. end point in time) of the T wave.

In some embodiments, the beat interval onset for the current beat isdefined as half the way, in time, between the previous beat and thecurrent beat.

In some embodiments, the beat interval offset for the current beat isdefined as half the way, in time, between the current beat and the nextbeat.

In some embodiments, a second segmentation step is performed on theresult of the previous segmentation. The product of this segmenting stepis referred to as ‘in-beat segments’.

In some embodiments, the number of in-beat segments is selected to beany integer number between one and the number of time samples in thecurrent beat. By way of non-limiting example, the number of in-beatsegment can be three.

In some embodiments, an automatic procedure is performed to detect theoptimal number of in-beat segments. In some embodiments, the automaticprocedure comprises using a Divide and Conquer algorithm, wherein eachsegment is divided into two equal sub-segments. If the energy content ofa sub-segment exceeds a pre-defined relative threshold, the sub-segmentitself is divided into two sub-segments and so on recursively. The stopcriterion can be reaching a minimum length of sub-segment, reaching amaximum number of segments or dividing into sub-segments such that allof them meet the energy threshold criterion.

In certain embodiments, the entropy measure is used to divide thesegment into sub-segments. In this case, the entropy of each segment isminimized.

In some embodiments, the result of the in-beat segmentation is thefollowing seven segments:

1. The isoelectric line from the beat onset to the onset of the P wave;

2. The P wave;

3. The isoelectric line from offset of the P wave to the onset of theQRS complex;

4. The QRS complex;

5. The isoelectric line from the offset of the QRS complex to the onsetof the T wave;

6. The T wave; and

7. The isoelectric line from the offset of the T wave to the beatoffset.

In some embodiments, the majority of the energy of the filtered N-ECGsignals data is in the QRS complex. Thus, in some embodiments, thein-beat segments a the following three in-beat segments:

1. From the beat onset to the onset of the QRS complex;

2. The QRS complex; and

3. From the offset of the QRS complex to the beat offset.

In some embodiments, the onset and offset of the QRS complex, for eachof the beats in the ECG signal, are automatically detected using adedicated method comprising: applying a digital band pass filter to thefiltered N-ECG signals data; calculating the curve length transform ofthe filtered N-ECG signals data; selecting the values of the data thatare higher than a threshold; calculating the first derivative of thetransformed data; finding the positive transitions and negativetransitions in the derivative data; finding pairs of thepositive-and-negative transition such that the distance between them, intime samples, is higher than a pre-selected threshold; setting the onsetof the QRS complex as the position, in time, of the positive transitionfrom the selected pair; and setting the offset of the QRS complex as theposition in time of the negative transition from the selected pair.

In some embodiments, the cutoff frequencies of the band pass filter areset to be 5 and 20 cycles/sec for the lower and upper frequenciesrespectively.

In some embodiments, the threshold is set to be a constant value of 0.3.In some embodiments, the threshold value is calculated automaticallydepending of the local characteristics of the data after the curvelength transform. In some embodiments, the local characteristic of thedata is the mean value of the transformed data.

In some embodiments, the segmentation also comprises an additionalcomplementary transformation that comprises changing the sample rate ofthe signal by an integer value. In some embodiments, the additionalcomplementary transformation reduces the variability of the resultingraw N-ECG fetal signals data caused by the fine positions of the onsetsand offsets of the selected segments.

In some embodiments the sample rate is decreased by a factor of 4 todecrease the calculation time. Alternatively, in certain embodiments,the sample rate is increased by a factor of 4.

In some embodiments, increasing the sample rate is done usinginterpolation. In some embodiments, increasing the sample rate is doneby zero padding the data and then applying a set of low pass FIRfilters.

In some embodiments, changing the sample rate of the signals is donebefore the segmentation procedure. In some embodiments, changing thesample rate is done after the segmentation. In these embodiments, theselected onsets and offsets of the different segments are refineddepending on the signals after changing the sample rate.

In some embodiments, the segmentation is updated depending on theconvergence of the iterative methods in the next steps describedhereinafter.

In some embodiments, the selected segments are modified using anonlinear parametric transformation in which the values of a predefinedset of parameters are changed according to some criterion.

In some embodiments, the values of a predefined set of parameters aredetermined and modified according to a method comprising: definingreference vectors as, for each filtered N-ECG signals data, the ECGsignal and its segments (called ‘measured potentials’ hereinafter); andfinding the values of the set of parameters that provide a good fit toresults of the measured potentials.

A “good fit” occurs when the difference between the adapted template andthe measured potentials is very small. In one embodiment, very small isdefined to be 10⁻⁵ or smaller. In other embodiments, “very small” isdefined as 10⁻⁶ or smaller or 10⁻⁴ or smaller or 10⁻⁷ or smaller orother exponents of 10 or other numbers.

In some embodiments, the difference between the adapted template and themeasured potentials is the L2 norm of the element-by-element subtractionbetween the two vectors.

In some embodiments, the values of a predefined set of parameters alsoinclude parameters that change the amplitude of a time signal.

In some embodiments, an iterative scheme is used to determine the valuesof the set of the parameters, comprising: selecting starting conditions;assigning tentative values to the set of parameters; adapting thetemplates using the set of parameters; comparing the adapted templatesto the measured potentials; checking if a stop criterion is reached;updating the values of the set of parameters if none of the stopcriteria are reached, and repeating steps steps (c), (d), (e) and (f).In some embodiments, if a stop criterion is reached, the iterativeprocedure is terminated and the current set of parameters is deemed theoptimal solution of the optimization problem.

In some embodiments, the starting conditions are set to be the ECGtemplates. In some embodiments, two templates are defined: The first isa global template that is defined for every ECG beat (see, for example,FIG. 35), wherein the template is calculated as a weighted average of Mbeats. In some embodiments, M is an integer number between 2 and thetotal number of beats in the ECG signal. In some embodiments, theweights used in the weighted average are equal yielding a normalaveraging. In some embodiments, the M beats fulfill the condition thatthe correlation coefficient between each of the beats should be higherthan a threshold. In certain embodiments, the threshold is set to be0.98. In some embodiments, before the averaging, the beats are shiftedand fine aligned using the exact position of the R-wave, the QRS onsetand the QRS offset. In some embodiments, the alignment procedure is doneusing the cross correlation function. In Some embodiments, the M beatsfulfill the condition that relation between the energy of the template,defined as an average of these beats, and the energy of the current ECGbeat is bounded close to 1. However, in some embodiments, the relationbetween the energy of the template and the energy of the current ECGbeat is higher than 1 by a small value or lower than 1 by a small value.

The second is a local template that is defined for each of thesub-segments in the current beat (see, for example, FIG. 36). In someembodiments, the template is calculated as the average of thesub-segments in the M-selected peaks. In some embodiments, the beats areselected to fulfill the conditions that the local heart rate is similarto the local heart rate for the current beat. By means of a non-limitingexample, the heart rate values are close by a factor of 1.5.

In some embodiments, the tentative values are set to random numbers. Insome embodiments, the tentative values are set to be constant numbers;by means of a non-limiting example, they are set to be 1.

In some embodiments, the tentative values are saved between differentexecutions of the algorithm and used if available.

In some embodiments, the different templates are adapted separatelydepending on the appropriate parameters that are assigned to thetemplates. In some embodiments, the templates are scaled by multiplyingthe templates vectors with the appropriate set of parameters. In someembodiments, the templates are shifted in time using the appropriate setof parameters.

In some embodiments, the Euclidian distance is used to compare the twosets of vectors; the adapted templates and the measured potentials fromthe processed N filtered signal data. In some embodiments, the costfunction is defined as the error energy function.

In some embodiments, the city-block measure is used to compare theadapted templates to the measured potentials.

In some embodiments, the cross correlation function is used to comparethe adapted templates and measured potentials. In some embodiments, a“good fit” is a correlation of 0.95 or greater.

In some embodiments, a maximum number of iterations is used as a stopcriterion.

In some embodiments, in the case of using the Euclidian distance as asimilarity measure, the value of the error energy function at a specificiteration is used as a stop criterion. In some embodiments, if the errorenergy becomes lower than a threshold, the stop criterion is reached.

In certain embodiments, the threshold is set to be constant. By means ofa non-limiting example, it is set to be 10⁻⁵.

In some embodiments, the threshold is calculated depending on thecharacteristics of the iterative procedure. For example, in someembodiments, the characteristics of the iterative procedure is themaximum number of allowed iterations; in other embodiments, thecharacteristics of the iterative procedure is the used cost function,for example the Euclidian distance function; and still otherembodiments, the characteristics of the iterative procedure is thenumber of time samples in the templates.

In some embodiments, an improvement measure is used as a stop criterion.

In some embodiments, the change in the set of parameters is used as animprovement measure. In some embodiments, if the values of the set ofparameters does not change, or change slightly, the stop criterion isfulfilled.

In some embodiments, the change in the value of the cost function, e.g.the error energy function, is used as an improvement measure.

In some embodiments, if a stop criterion is reached, an optimizationscheme is applied to update the values of the set of parameters.

In some embodiments, when using the Euclidian distance as a similaritymeasure, the optimization problem becomes a non-linear least squaresproblem. In some embodiments, the solution of this problem gives theoptimal set of parameters that minimizes the cost function taken as theEuclidian distance.

In some embodiments, Gauss-Newton algorithm is used to solve thenon-linear least squares problem.

In some embodiments, the Steepest-Descent (Gradient-Descent) method isused to solve the non-linear least squares problem.

In some embodiments, the Levenberg-Marquardt algorithm is used to solvethe non-linear least squares problem. In these embodiments, the problembecomes a damped least squares problem. Levenberg-Marquardt algorithm isa method, which interpolates between the Gradient-Descent andGauss-Newton algorithm taking advantage of both methods to increase theconvergence accuracy and decrease the convergence time.

In some embodiments, Gradient-Descent, Gauss-Newton algorithm andLevenberg-Marquardt algorithm, are iterative methods that are repeated anumber of times, potentially, bringing the Euclidian distance (the errorenergy function) to a local minima.

In some embodiments, updating the set of parameters is performed usingthe relation:

P _(k+1) =P _(k)−[

_(k) ^(T)

_(k)+λ_(i)·diag(

_(k) ^(T)

_(k))]⁻¹*

_(k) ^(T)[ϕ_(c)(P _(k))−ϕ_(m)]

Where P_(k) is the set of parameters at the kth iteration; and

ϕ_(c)(P_(k)) is the adapted version of the templates for the P_(k)parameters; and

λ_(i) is the damping parameter in the Levenberg-Marquardt algorithm; and

ϕ_(m) is the measured potentials (measured ECG signals); and

_(k) is the Jacobian matrix calculated for the set of parameters P_(k)by altering the values of the parameters; and

diag(

_(k) ^(T)

_(k)) is the diagonal of the approximated Hessian matrix.

In some embodiments, eliminating the modified segments from the N rawsignal data is achieved by subtracting the adapted templates from themeasured potentials. In some embodiments, the subtraction results in Nraw fetal signal data. In some embodiments, the N raw fetal signal datacomprises noise and fetal cardiac electrical activity data.

In some embodiments, maternal cardiac activity is eliminated using thealgorithm shown in FIG. 33.

In some embodiments, maternal cardiac activity is eliminated using thealgorithm shown in FIG. 34.

A representative result of maternal ECG elimination according to someembodiments of the present invention is shown in FIG. 37.

Extraction of the Fetal Cardiac Electrical Activity Data and DetectingFetal Cardiac Electrical Activity Data

In some embodiments, raw fetal ECG signals data is extracted andanalyzed by utilizing a Blind-Source-Separation (BSS) algorithm on (1)the filtered N-ECG signals data and (2) the corrected N-ECG signalsdata, wherein the raw fetal ECG signals data comprises a N number offetal ECG signals (N-ECG fetal signals); processing, by the at least onecomputer processor, the raw fetal ECG signals data to improve asignal-to-noise ratio by at least: i) applying a band-pass filter withina range of 15-65 Hz to break the plurality of raw N-ECG fetal signals into a plurality of frequency channels, ii) scoring an ECG fetal signalper channel based on a peak-2-mean analysis to identify a plurality offetal heartbeat channels, wherein each fetal heartbeat channelcorresponds to a particular fetal ECG fetal signal; and iii) selectingthe identified plurality of fetal heartbeat channels into filtered fetalECG signals data, comprising a N number of filtered fetal ECG signals(filtered N-ECG fetal signals data); detecting, by the at least onecomputer processor, fetal heart peaks in the filtered N-ECG fetalsignals data, by performing at least: i) dividing each filtered N-ECGfetal signal into a first plurality of fetal ECG signal segments; ii)normalizing a filtered fetal ECG signal in each fetal ECG signalsegment; iii) calculating a first derivative of the filtered fetal ECGsignal in each fetal ECG signal segment; and iv) finding local fetalheart peaks in each fetal ECG signal segment based on determining azero-crossing of the first derivative; calculating, by the at least onecomputer processor, based on detected fetal heart peaks, at least oneof: i) fetal heart rate, ii) fetal heart curve, iii) beat-2-beat fetalheart rate, or iv) fetal heart rate variability; and outputting, by theat least one computer processor, a result of the calculating step.

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio comprises: i) applying a band-passfilter within a range of 15-65 Hz to break the plurality of N-ECG fetalsignals in to a plurality of frequency channels, ii) scoring an ECGfetal signal per channel based on a peak-2-mean analysis to identify aplurality of fetal heartbeat channels, wherein each fetal heartbeatchannel corresponds to a particular fetal ECG fetal signal; and iii)selecting the identified plurality of fetal heartbeat channels intofiltered ECG fetal signals data, comprising a N number of filtered fetalECG signals (filtered N-ECG fetal signals data).

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio comprises: i) applying a band-passfilter within a range of 1-70 Hz to break the plurality of N-ECG fetalsignals in to a plurality of frequency channels, ii) scoring an ECGfetal signal per channel based on a peak-2-mean analysis to identify aplurality of fetal heartbeat channels, wherein each fetal heartbeatchannel corresponds to a particular fetal ECG fetal signal; and iii)selecting the identified plurality of fetal heartbeat channels intofiltered ECG fetal signals data, comprising a N number of filtered fetalECG signals (filtered N-ECG fetal signals data).

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio comprises: i) applying a band-passfilter within a range of 5-70 Hz to break the plurality of N-ECG fetalsignals in to a plurality of frequency channels, ii) scoring an ECGfetal signal per channel based on a peak-2-mean analysis to identify aplurality of fetal heartbeat channels, wherein each fetal heartbeatchannel corresponds to a particular fetal ECG fetal signal; and iii)selecting the identified plurality of fetal heartbeat channels intofiltered ECG fetal signals data, comprising a N number of filtered fetalECG signals (filtered N-ECG fetal signals data).

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio comprises: i) applying a band-passfilter within a range of 10-70 Hz to break the plurality of N-ECG fetalsignals in to a plurality of frequency channels, ii) scoring an ECGfetal signal per channel based on a peak-2-mean analysis to identify aplurality of fetal heartbeat channels, wherein each fetal heartbeatchannel corresponds to a particular fetal ECG fetal signal; and iii)selecting the identified plurality of fetal heartbeat channels intofiltered ECG fetal signals data, comprising a N number of filtered fetalECG signals (filtered N-ECG fetal signals data).

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio comprises: i) applying a band-passfilter within a range of 1-65 Hz to break the plurality of N-ECG fetalsignals in to a plurality of frequency channels, ii) scoring an ECGfetal signal per channel based on a peak-2-mean analysis to identify aplurality of fetal heartbeat channels, wherein each fetal heartbeatchannel corresponds to a particular fetal ECG fetal signal; and iii)selecting the identified plurality of fetal heartbeat channels intofiltered ECG fetal signals data, comprising a N number of filtered fetalECG signals (filtered N-ECG fetal signals data).

In some embodiments, the processing of the raw N-ECG fetal signals datato improve the signal-to-noise ratio further comprises at least oneof: 1) utilizing a Singular-Value-Decomposition (SVD) technique; or 2)utilizing a Wavelet-Denoising (WD) technique.

In some embodiments, all of the raw N-ECG signals data are used in theBSS procedure so that the BSS procedure is applied to 2N signals. Inother embodiments, only part of the raw N-ECG signals data is used inthe BSS procedure so that the BSS procedure is applied to between N+1signals and less than 2N signals.

In some embodiments, Principal Component Analysis (PCA) is used as theBSS method. In some embodiments, Independent Component Analysis (ICA) isused as the BSS method.

In some embodiments, the results of the BSS procedure is furtheranalyzed to improve the signal to noise ratio of the signals. In someembodiments, the further analysis includes a band-pass filter in therange of 15-65 cycle/second. In some embodiments, the further analysisincludes applying Singular-Value-Decomposition (SVD). In someembodiments, the further analysis includes applying Wavelet-Denoising.

In some embodiments, the further analysis includes applying‘peak-2-mean’ transformation, which in some versions is in a separatesoftware module, that comprises:

-   -   a. using a moving window (win) to calculate the relation:

$\frac{\max ( {{Signal}\lbrack{win}\rbrack} )}{{mean}( {{Signal}\lbrack{win}\rbrack} )},$

and

-   -   b. calculating the 1st derivative of the result of step a; and    -   c. finding the zero crossing to find peaks taking the negative        part of the derivative; and    -   d. finding the peaks in the previous result and cluster them        based on the difference between them. Identify the best group as        the group with the maximum score defined as: SCR=n/RMS where n        is the number of peaks in each cluster and RMS is the energy of        the derivative of the distances between the peaks; and    -   e. performing a prediction for the RR interval for the best        group; and    -   f. if the prediction doesn't fit the real physiological model of        the fetal RR intervals, the current signal is ignored and one        then begins the process at the beginning (i.e. using a moving        window to calculate the relation) with the next signal; and    -   g. if the prediction does fit the real physiological model of        the fetal RR, calculate the auto correlation function of a        windowed RMS of the signal. If the result has a narrow        distribution, the current signal is ignored, and if the result        does not have a narrow distribution, then proceed as follows:    -   h. applying AGC to the negative derivative signal; and    -   i. normalizing the result.

In certain embodiments, the above ‘peak-2-mean transformation’ isapplied separately to each signal (i.e. each of the 2N signals or eachof the between N+1 and less than 2N signals).

In some embodiments, fetal heart beat detection is performed. Forexample, in some embodiments, the N raw fetal signal data are subjectedthe fetal peak detection procedure before the further analysis and inother embodiments the results of the further analysis subjected to thefetal peak detection procedure.

In some embodiments, the analysis comprises: dividing the N filteredfetal signal data into segments; normalizing the signal data in eachsegment; calculating the first derivative of the normalized signal data;identifying the fetal ECG peaks within the normalized signal data bydetermining the zero-crossing of the first derivative; excluding peakswhose absolute value is less than a threshold pre-selected by the user;and excluding very close peaks where the distance between them is lessthan a threshold, thereby obtaining processed N filtered fetal signaldata.

In some embodiments, the length of each segment is set at 10 seconds.

In some embodiments, the length of each segment is selectedautomatically depending on the length of the recording.

In some embodiments, the signal data in each segment is normalized bythe absolute maximum value of the signal data. In some embodiments, thesignal data in each segment is normalized by the absolute non-zerominimum value of the signal data.

In some embodiments, a first order forward derivative is used. In someembodiments, a first order central derivative is used.

In some embodiments, the threshold is selected to be a constant value of0.3.

In some embodiments, the threshold is selected depending on the localcharacteristics of the signal data. In some embodiments, the localcharacteristic of the signal is the median value of the signal data orany multiplication of this value. In some embodiments, the localcharacteristic of the signal is the mean value of the signal data or anymultiplication of this value.

In some embodiments, the threshold on the distance is selected to be 100samples.

In some embodiments, the local characteristics of the signal can be themaximum predicted RR internal or any multiple of this value.

In some embodiments, a “peaks array” is generated from the filteredN-ECG fetal signals data. In some embodiments, the peak array comprisesthe number of detected peaks for each of the segments of the filteredN-ECG fetal signals data.

In some embodiments, clustering is performed on the peaks array. In someembodiments, k-means clustering is used to group the peaks into a numberof clusters. In some embodiments, k-medoids clustering is used to groupthe peaks into a number of clusters.

In some embodiments, the number of clusters for the clustering is set tobe three. In some embodiments, the number of clusters for the clusteringis selected automatically depending on the characteristics of thefiltered N-ECG fetal signals data.

In some embodiments, the clustering is used to exclude outliers. In someembodiments, outliers are peaks that have anomalous characteristics.

In some embodiments, the characteristic is the distance between a peakand its neighboring peaks. In some embodiments, the characteristic isthe amplitude of the peak.

In some embodiments, a new peak array is constructed after the exclusionof the anomalous peaks.

In some embodiments, the new peak array is further analyzed and thepeaks are scored depending on the signal to noise ratio of the filteredN-ECG fetal signals data.

In some embodiments, the signal to noise ratio score is calculated bycalculating the relative energy of the QRS complexes from the overallenergy of the filtered N-ECG fetal signals data.

In some embodiments, the detected peaks for each of the filtered N-ECGfetal signals data are fused for a more robust detection. In someembodiments, the fusion of the detected peaks is done using the scoresgiven for each of the peaks of filtered N-ECG fetal signals data.

In some embodiments, a global array of peaks is defined using the fusedpeaks.

In some embodiments, the peaks of each of the filtered N-ECG fetalsignals data is redetected and the positions are refined using theglobal peaks array. In some embodiments, the global peak array isconstructed based on the best lead with corrections made using the peaksfrom the other leads and the global peaks array is examined usingphysiological measures, such as, for example, RR intervals, HR, HRV).

In some embodiments, the beat-to-beat fetal heart rate is extracted fromthe detected fetal peaks positions. In some embodiments, the fetal heartcurve is also extracted from the detected fetal peak positions.

PCG Signals Data Processing According to Some Embodiments of the PresentInvention

In some embodiments, the present invention provides a system fordetecting, recording and analyzing aucoustic data from a pregnant mothercarrying a fetus to discern cardiac data of mother and/or fetus. In someembodiments, a plurality of acoustic sensors is used to recordphonocardiogram (PCG) signals, representative of cardiac activity data.The phonocardiogram signals are recordings of all the sounds made by theheart during a cardiac cycle due to, typically, for example, vibrationscreated by closure of the heart valves. For instance, there can be atlist two sound generating events: the first when the atrioventricularvalves close at the beginning of systole (S1) and the second when theaortic valve and pulmonary valve close at the end of systole (S2).

In some embodiments, the exemplary inventive system of the presentinvention can utilize a plurality of acoustic sensors (e.g., 2, 3, 4, 5,6, 7, 8, 9, 10, etc.) to be positioned on and/or near the abdomen of apregnant woman. For example, in some embodiments, the acoustic sensorsare directly attached. In some embodiments, the acoustic sensors areincorporated into an article, such as, for example, a belt, a patch, andthe like, and the article is worn by, or placed on, the pregnant mother.

In some embodiments, the choice of acoustic sensors is readilydetermined by one of ordinary skill in the art. Factors influencingacoustic sensor choice include, but are not limited to, the sensitivityof the microphone component, the size of the acoustic sensor, the weightof the acoustic sensor, and the like. In some embodiments, the acousticsensor is configured to compensate for the changes in sound propagationcaused by the skin-air interface. Acoustic signals comprise sound wavesor vibrations that propagate as a mechanical wave of pressure anddisplacement, through a medium such as air, water, or the body. Withoutintending to be limited to any particular theory, the behavior of soundpropagation can be affected by the relationship between the density andpressure of the medium though which the sound wave propagates. Also, thebehavior of sound propagation can be affected by the motion of themedium though which the sound wave propagates. Furthermore, the behaviorof sound propagation can be affected by the viscosity of the mediumthough which the sound wave propagates.

For example, as shown in FIG. 29, the exemplary inventive system of thepresent invention utilizes a set of four acoustic sensors (M1-M4) atrespective exemplary positions. In some embodiments, the positioning ofacoustic sensors can varies based, at least in part, on, for example,shape of mother's stomach, the stage of the pregnancy, physiologicalcharacteristics of mother and/or fetus(es), previous acoustic and/orother types of cardio recordings (e.g., Electrocardiogram (ECG) signalrecordings and analysis, etc.), and other similarly suitable data.

In some embodiments, the acoustic sensors of the present inventionrecord the internal sound produced inside the woman with added noisefrom the environment. As detailed below, from these recordings theheartbeat sound of the fetus(es) and/or the mother are extracted and theheart rate of each subject is calculated.

In some embodiments, the level of detection by each acoustic sensor isindependent of the other acoustic sensors (e.g, in FIG. 29, on otherthree acoustic sensors). Referring to FIG. 29, in some embodiments, itis determined that, typically, the fetus PCG signals are detected by theacoustic sensors in locations M3 and/or M4, while the maternal PCGsignals are detected by the acoustic sensors in locations M1 and/or M2.In some embodiments, the maternal PCG signals can be detected by allfour sensors (M1-M4) and have to be cleaned in order to detect thefetus(es) heartbeats. In some embodiments, as detailed below, thecleaning process is performed using at least one Independent componentanalysis (ICA) algorithm of the present invention. For instance, in someembodiments, the inventive system of the present invention assumes thatthe interfering noises are audio sources which are not the fetal originthat thus are statistically independent from the fetal heart sounds.

An Example of an Illustrative Process Utilized to Estimate the Fetus(es)and/or Maternal Heartbeat Data in Accordance with the Present Invention

FIG. 38 shows an exemplary process utilized, in some embodiments, by theexemplary inventive system of the present invention to estimate thefetus(es) and/or maternal heartbeat data (e.g., presence of heartbeats,heartbeat patterns, heartbeat frequency, ect.).

Referring to FIG. 38, the illustrative process contains at the followingfour stages.

Stage 1: Filter Bank

Referring to FIG. 38, the input is the signal data recorded by eachacoustic sensor (M1-M4) shown in FIG. 29. In some embodiments, therecorded signal data from each of the four acoustic sensors is passedthrough, for example but not limited to, L number of bandpass filters,where each filter has a predetermine frequency range. In someembodiments, the exemplary six bandpass filters (L=6) can have thefollowing six bandwidths:

10-45 Hz

20-50 Hz

25-65 Hz

40-80 Hz

55-95 Hz

20-80 Hz.

In some embodiments, there can be at least 3 bandpass filters. In someembodiments, there can be between 3-12 bandpass filters. In someembodiments, there can be between 5-10 bandpass filters. In someembodiments, there can be at least 5 bandpass filters. In someembodiments, there can be at least 7 bandpass filters.

In some embodiments, the bandwidth of a particular filter can beselected from a range of 10-100 Hz. In some embodiments, the bandwidthof a particular filter can be selected from a range of 20-100 Hz. Insome embodiments, the bandwidth of a particular filter can be selectedfrom a range of 30-100 Hz. In some embodiments, the bandwidth of aparticular filter can be selected from a range of 40-100 Hz. In someembodiments, the bandwidth of a particular filter can be selected from arange of 50-100 Hz. In some embodiments, the bandwidth of a particularfilter can be selected from a range of 60-100 Hz. In some embodiments,the bandwidth of a particular filter can be selected from a range of70-100 Hz. In some embodiments, the bandwidth of a particular filter canbe selected from a range of 80-100 Hz. In some embodiments, thebandwidth of a particular filter can be selected from a range of 90-100Hz.

In some embodiments, a particular filter can have a frequency range of5-110 Hz. In some embodiments, a particular filter can have a frequencyrange of 15-110 Hz. In some embodiments, a particular filter can have afrequency range of 25-110 Hz. In some embodiments, a particular filtercan have a frequency range of 35-110 Hz. In some embodiments, aparticular filter can have a frequency range of 45-110 Hz. In someembodiments, a particular filter can have a frequency range of 55-110Hz. In some embodiments, a particular filter can have a frequency rangeof 5-105 Hz. In some embodiments, a particular filter can have afrequency range of 5-100 Hz. In some embodiments, a particular filtercan have a frequency range of 5-95 Hz. In some embodiments, a particularfilter can have a frequency range of 5-25 Hz. In some embodiments, aparticular filter can have a frequency range of 5-50 Hz. In someembodiments, a particular filter can have a frequency range of 5-75 Hz.

In some embodiments, referring to FIG. 38, a K number of outputs(filtered PCG outputs) from each of the six filters are forwarded to thesecond stage. For example, in some embodiments, detailed below, K isequal to, but not limited to, 4. In some embodiments, K is equal to thenumber of acoustic sensors (M1-M4) being used to collect the acousticsensor data. In some embodiments, a predetermined number of outputs isforwarded to the second stage.

Stage 2: Wavelet Denoising

In some embodiments, for example, one of the four filtered PCG outputsfrom the six bandpass filters is further subject to wavelet denoising,where such particular filtered PCG output is deconstructed by passingthrough a succession of low and high pass filters up to X times to formthe deconstructed filtered PCG output. In some embodiments, the value ofX depends on a sampling frequency which allows to achieve suitableresults to meet requirements detailed below. For example, in someembodiments, the particular filtered PCG output is deconstructed bypassing through a succession of low and high pass filters up to 3 times.For example, in some embodiments, the particular filtered PCG output isdeconstructed by passing through a succession of low and high passfilters up to 5 times. For example, in some embodiments, the particularfiltered PCG output is deconstructed by passing through a succession oflow frequency pass and high frequency pass filters up to 6 times.

FIGS. 39A and 39B show illustrative diagrams of the exemplary denoisingstage. In some embodiments, the result of the denoising is the wavelettransform coefficients called approximations and details. Specifically,the deconstructed filtered PCG output can be reconstructed using thedetails: Dj(n), j=1, . . . , N and the approximation Aj(n) to form thedenoised filtered PCG output.

In some embodiments, the denoised filtered PCG output is determinedbased on equation 1:

y _(r)(n)=A _(N)(n)+Σ_(j=1) ^(N) D _(j)(n)  (1).

Specifically, initially, in the first step of the denoising, all detailsthat most likely do not carry information of the heartbeat signal areset to 0, for example but not limited to: D_j(n)=0, j=1, 2, 3, 5, 6, . .. , N. For example, details that do not contain sound data correspondingto S1 and/or S2 phases of the cardiac contraction cycle.

Then, the remaining detail, the fourth detail, is thresholded using athreshold calculated utilizing, for example but not limited to, Stein'sUnbiased Risk Estimate (SURE) method illustrated by equation 2:

$\begin{matrix}{{D_{4}(n)} = \{ \begin{matrix}\begin{matrix}0 & \forall\end{matrix} & {{D_{4}(n)} < {TH}_{SURE}} \\{D_{4}(n)} & {Otherwise}\end{matrix} } & (2)\end{matrix}$

In some embodiments, the threshold can be calculated using any othersimilarly suitable method.

In some embodiments the remaining detail can be any of the details or acombination of them.

Lastly, the denoised filtered PCG output is calculated using equation(1).

Stage 3: ICA Transformation

Referring to FIG. 38, (1) the filtered PCG output and (2) the denoisedfiltered PCG output are further transformed using at least one ICAalgorithm to form (1) the transformed filtered PCG output and (2) thetransformed denoised filtered PCG output, respectively. In someembodiments, the exemplary ICA algorithm is, for example but not limitedto, the FAST ICA algorithm. In some embodiments, the FAST ICA algorithmis, for example, utilized in accordance with “Independent componentanalysis: Algorithms and applications,” Hyvarinen et al., NeuralNetworks 13 (4-5): 411-430 (2000), whose specific descriptions arehereby incorporated herein for such specific purpose.

The term “transformation(s)” as used herein refers to linear ornon-linear mathematical transformations, which may include, inter alia,digital filtering, mathematical linear or non-linear decomposition,mathematical optimization.

Stage 4: Detection of Fetal and Maternal Heart Beats

To summarize, at this stage, all outputs of the filterbank, denoisng,and ICA, from the previous three stages are being utilized as a S numberof detection heartbeat (DH) inputs, where S is a number of all outputsof the filterbank, denoisng, and ICA stages, calculated as L×M, asdetermined below:

-   -   1) M filtered PCG outputs, resulted from 4 signal data inputs of        the acoustic sensors M1-M4 being passed through L bandpass        filters;    -   2) M filtered ICA transforms of the filtered PCG outputs;    -   3) M denoised filtered PCG outputs; and    -   4) M denoised filtered ICA transforms of the denoised filtered        PCG outputs, where M=L×K.

In some embodiments, the exemplary inventive system of the presentdetection assumes that the heart beats of the mother and/or thefetus(es) can be detected in each one of these DH inputs. In someembodiments, the exemplary inventive system has no prior historical datato suggest in which of these DH inputs the heartbeat of the fetus(es)would be detected, or the heartbeat of the mother, or both, or none.

For example, in some embodiments, the L number of filters in the filterbank is 6 and the total number of inputs for stage 4 is M×L=96.

In some embodiments, the stage 4 is further divided into foursub-stages:

-   -   Sub-stage 1: Detect all heartbeats in each of the DH inputs;    -   Sub-stage 2: Calculate a confidences score that describes the        probability that the heartbeats detected in Step 1 are actual        heartbeats and not the noise;    -   Sub-stage 3: Divide all the DH inputs into at least two groups:        -   Group 1: fetal heartbeat outputs that contain fetal            heartbeats        -   Group 2: maternal heartbeat outputs that contain maternal            heartbeats;    -   Sub-stage 4: Select from each group the most probable output        that contains the corresponding heartbeat.

Sub-Stage 1: Heartbeat Detection

Typically, acoustic signal can contain at least some noise andheartbeats in at least some cases can be “covered” with noise or in avery noisy surrounding. In addition, typically, the heartbeat morphologycan vary form one heartbeat to the next. In some embodiments, theSub-stage 1 is carried out in at least the following number of steps.

Step 1: Beat Detection

-   -   i. In some embodiments, the exemplary inventive system of the        present invention calculates a slow envelope of the absolute of        the Hilbert transform of the acoustic signal. For instance,        exemplary inventive system of the present invention calculates        this envelope by applying a moving average filter on the        absolute value of the Hilbert transform. In some embodiments,        the exemplary inventive system of the present invention can        utilize the moving average window having a predetermined length        of P. For example, in some embodiments, P is 300 milliseconds        (ms). In some embodiments, P varies from 100 to 500 ms. In some        embodiments, P varies from 200 to 500 ms. In some embodiments, P        varies from 300 to 500 ms. In some embodiments, P varies from        400 to 500 ms.    -   ii. The exemplary inventive system of the present invention then        determines all the peaks of the signal by identifying the zero        crossings of the derivative of the signal in each respective DH        input.    -   iii. The exemplary inventive system of the present invention        then discards all peaks that are not prominent enough, based, at        least in part, on the following criteria used is:

${{Select}\mspace{14mu} {\forall{{{peaks}\mspace{14mu} {such}\mspace{14mu} {that}\mspace{14mu} \frac{p(k)}{{median}( \lbrack {{p(1)},{p(2)},\ldots \;,{p(N)}} \rbrack )}} > {{Thresh}\mspace{14mu} k}}}} = {1\mspace{11mu} \ldots \mspace{11mu} N\mspace{14mu} {where}\mspace{14mu} {p(k)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {prominence}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} k^{th}\mspace{14mu} {{peak}.}}$

-   -   iv. The exemplary inventive system of the present invention then        groups all peaks into two groups according to, for example,        shape and size. For example, such grouping is done using a        Gaussian Mixture model clustering algorithm with the following        features for each peak:        -   the width of each peak divided by the max peak width, and        -   the height of the peak divided by its prominence.    -   v. The exemplary inventive system of the present invention then        selects a group of peaks that has the smallest variance measure        as the peaks for the next steps.    -   vi. The exemplary inventive system of the present invention then        repeats steps i-v using an average window length R ms. In some        embodiments, the average window length of 250 ms. In some        embodiments, the average window length is between 100-300 ms.    -   vii. The exemplary inventive system of the present invention        then calculates the Excess Kurtosis measure of the distribution        (an adjusted version of Pearson's kurtosis) for the locations of        peaks, and selects the peak locations that provide the smallest        Excess Kurtosis. FIG. 40 shows the result of the detection at        this step, specifically the beat located in a noisy area is not        detected.    -   viii. From the selected peaks locations, the exemplary inventive        system of the present invention then calculates an initial        estimate of the average Heart Rate (HR).

Step 2—Enhanced Detection

The exemplary inventive system of the present invention refines thedetection results of Step 1 by, for example, adding missing beats andcorrecting the Step 1 detection locations.

-   -   i. The exemplary inventive system of the present invention        calculates a fast envelope using a Q ms window in the same        manner performed as detailed in item (i) of Step 1. For example,        in some embodiments, Q is 100 ms. In some embodiments, Q varies        between 50-200 ms. In some embodiments, Q varies between 50-150        ms. In some embodiments, Q varies between 50-100 ms.    -   ii. The exemplary inventive system of the present invention then        identifies peaks on the fast envelope signal and selects all        prominent peaks in same manner as detailed in item (iii) of Step        1.    -   iii. The exemplary inventive system of the present invention        then calculates all beat to beat intervals (B2B) from the        detection results of Step 1, where B2B is an interval between        successive beats.    -   iv. Referring to FIG. 41, the exemplary inventive system of the        present invention then completes missing beats which are not        identified in Step 1, by reiteratively analyzing all beat to        beat intervals determined in the item (iii) of Step 2 based on        at least the following exemplary, but not limiting, conditions:        -   if B2B(k) is higher than 1.5×B2B(average) calculated in            item (viii) of Step 1, find a peak from the fast envelope            which is as close as possible to the last peak found in            item (viii) of Step 1+B2B(average).    -   v. The exemplary inventive system of the present invention then        corrects the detection of Step 1, using the fast envelope.

In some embodiments, the exemplary inventive system of the presentinvention performs Step 2 to the extent necessary to minimize the beatto beat variance.

Sub-Stage 2: Calculate a Confidence Score for the Heartbeat Detection

Typically, a beat-by-beat heart rate is a physiological signal whichcannot change too fast and usually follows a baseline that defines anaverage heart rate. In some embodiments, the exemplary inventive systemof the present invention utilizes the average heart rate as the base forcalculating the confidence score. For each detection of heart beats (forexample but not limited to 96 of them, obtained at the end of Sub-Stage1 from 96 DH inputs), the exemplary inventive system of the presentinvention calculates the confidence score in accordance with thefollowing steps.

-   -   i) The exemplary inventive system of the present invention        generates a beat-by-beat heartbeat graph (shown in FIG. 42)        based on the locations of the beats.    -   ii) The exemplary inventive system of the present invention then        estimates the heart rate for the whole signal (HRestim), by        calculating a histogram of all the beat-by-beat heart rates,        where the estimated heart rate for the whole signal is at the        maximum of the histogram.    -   iii) The exemplary inventive system of the present invention        then estimates a base line for the beat-by-beat heartbeat graph        (FIG. 42) using a median filter (e.g., order 20, etc.).    -   iv) The exemplary inventive system of the present invention then        calculates the confidence score based on the following equation        (3):

$\begin{matrix}{{{Score} = \sqrt{\frac{{MfRR}^{2} + {SfRR}^{2} + {OvMP}^{2}}{3}}},{{{where}\mspace{14mu} {MfRR}} = \frac{\sum_{n = 1}^{N}{{{{HR}(n)} - {HRestim}}}}{N}},{{SfRR} = {{std}( {{{{HR}(n)} - {HRestim}}} )}},{{OvMP} = {\frac{1}{N}{\sum 1_{HR}}}},{1_{HR} = \{ {\begin{matrix}{1,} & {{{{{HR}(n)} - {HRestim}}} < {{HRestim} \times 0.15}} \\{0,} & {otherwise}\end{matrix}.} }} & (3)\end{matrix}$

Sub-Stage 3: Group all Heartbeat Detections into 2 Groups: Maternal andFetal

Based on the Stages 1-3 and Sub-stages 1-2, The exemplary inventivesystem of the present invention generates S beat-by-beat heartbeatgraphs, which are vectors representative of the DH inputs obtained fromStages 1-3 (DH input vectors). For example, in case of 96 DH inputs, 96beat-by-beat heartbeat graphs is generated. Some of the DH input vectorsrepresent maternal heartbeat and some represent the fetal heartbeat.

In some embodiments, the exemplary inventive system of the presentinvention can utilize, for example, other contemporaneous maternalheartbeat data about the maternal heartbeat which has been separatelydetermined based on data collected from non-acoustic sensor(s)/equipment(e.g., ECG data) to group all DH input vectors that are highlycorrelated with the other contemporaneous maternal heartbeat data ascandidates for maternal heart rate detection and the remaining DH inputvectors are grouped as fetal heartbeat candidates.

In some embodiments, when the other contemporaneous maternal heartbeatdata is not available, the exemplary inventive system of the presentinvention can group the DH input vectors, by clustering the estimatedheart rate of each DH input vector according to its value. In someembodiments, the exemplary inventive system of the present inventionthen designates the DH input vectors with a higher average heartbeatrate into the fetal heartbeat group.

Sub-Stage 4: Select the Fetal and Maternal Best Detections

In some embodiments, based on the best confidence score, the exemplaryinventive system of the present invention selects the bestrepresentative maternal DH input vector as the best detection of thematernal heartbeat rate from the group of the maternal heartbeat DHinput vectors identified in Sub-stage 3.

In some embodiments, based on the best confidence score, the exemplaryinventive system of the present invention selects the bestrepresentative fetal DH input vector as the best detection of the fetalheartbeat rate from the group of the fetal DH input vectors identifiedin Sub-stage 3.

FIG. 43 shows an illustrative example a beat-by-beat heartbeat graphrepresentative of an exemplary DH input vector with a low confidencescore.

FIG. 44 shows an illustrative example a beat-by-beat heartbeat graphrepresentative of an exemplary DH input vector with a high confidencescore.

In some embodiments, the instant invention is directed to acomputer-implemented method which includes at least the steps of:receiving, by at least one computer processor executing specificprogrammable instructions configured for the method, a plurality ofPhonocardiogram (PCG) signals data inputs from a plurality of acousticsensors; digital signal filtering, by the at least one computerprocessor, utilizing a plurality of bandpass filters, the plurality ofPCG signals data inputs to form a plurality of filtered PCG outputs,where the plurality of bandpass filters includes a L number of bandpassfilters, where each bandpass filter outputs a K number of filtered PCGoutputs; wavelet denoising, by the at least one computer processor, afirst subset of filtered PCG outputs of the plurality of filtered PCGoutputs to form a M number of denoised filtered PCG outputs, where M isequal to L multiply by K; transforming, by the at least one computerprocessor, utilizing an Independent-Component-Analysis (ICA), a secondsubset of filtered PCG outputs of the plurality of filtered PCG outputsto form the M number of filtered ICA transforms; transforming, by the atleast one computer processor, utilizing theIndependent-Component-Analysis (ICA), a first portion of the secondsubset of denoised filtered PCG outputs to form the M number of filtereddenoised filtered ICA transforms; compiling, by the at least onecomputer processor, a S number of a plurality of detection heartbeat(DH) inputs, including: i) the M number of filtered PCG outputs, ii) theM number of denoised filtered PCG outputs, iii) the M number of filteredICA transforms, and iv) the M number of denoised filtered ICAtransforms; detecting, by the at least one computer processor, beatlocations of beats in each of DH inputs; calculating, by the at leastone computer processor, a confidence score that describes a probabilitythat the beats in each DH input of the plurality of DH inputs representactual heartbeats and not a noise; dividing, by the at least onecomputer processor, the plurality of DH inputs into at least two groups:i) a first group of DH inputs containing fetal heartbeats, ii) a secondgroup of DH inputs containing maternal heartbeats selecting, by the atleast one computer processor, from the first group of DH inputs, atleast one particular fetal DH input that contains the fetal heartbeatbased on a first confidence score of the at least one particular fetalDH input; and selecting, by the at least one computer processor, fromthe second group of DH inputs, at least one particular maternal DH inputthat contains the maternal heartbeat, based on a second confidence scoreof the at least one particular maternal DH input.

In some embodiments, where the wavelet denoising includes at least:deconstructing, by the at least one computer processor, each filteredPCG output to generate a plurality of transform coefficients, byirrelatively passing each filtered PCG output through a succession oflow and high pass filters; identifying, by the at least one computerprocessor, a subset of heartbeat-carrying transform coefficients fromthe plurality of transform coefficients, reconstructing, by the at leastone computer processor, the subset of heartbeat-carrying transformcoefficients to form the M number of denoised filtered PCG outputs.

In some embodiments, K is equal to a number of the plurality of acousticsensors, and L is equal to 6.

In some embodiments, each filter of the plurality of bandpass filtershas a bandwidth of 10-100 Hz and a frequency range of 5-110 HZ.

In some embodiments, the detecting of the beat locations of the beats ineach of DH inputs includes at least: calculating, by the at least onecomputer processor, a predetermined transform filter; iterativelyrepeating, by the at least one computer processor, based on apredetermined average window length of the predetermined transformfilter: identifying, in each DH input, a subset of peaks having apredetermined shape and a predetermined size, and selecting a group ofpeaks from the subset of peaks, where the group of peaks has thesmallest variance measure; calculating, by the at least one computerprocessor, initial locations of the beats.

In some embodiments, the calculating the confidence score includes atleast: generating, by the at least one computer processor, abeat-by-beat heartbeat graph for each DH input based on the beatlocations.

In some embodiments, the dividing the plurality of DH inputs into thefirst group of DH inputs containing fetal heartbeats and the secondgroup of DH inputs containing maternal heartbeats includes at least:clustering, by the at least one computer processor, the beats of each DHinput according to each beat value; and assigning, by the at least onecomputer processor, a particular DH input having a higher average beatrate into the first group of DH inputs containing fetal heartbeats.

In some embodiments, the present invention is directed to a specificallyprogrammed computer system, including at least the following components:at least one specialized computer machine, including: a non-transientmemory, electronically storing particular computer executable programcode; and at least one computer processor which, when executing theparticular program code, becomes a specifically programmed computingprocessor that is configured to at least perform the followingoperations: receiving a plurality of Phonocardiogram (PCG) signals datainputs from a plurality of acoustic sensors; digital signal filtering,utilizing a plurality of bandpass filters, the plurality of PCG signalsdata inputs to form a plurality of filtered PCG outputs, where theplurality of bandpass filters includes a L number of bandpass filters,where each bandpass filter outputs a K number of filtered PCG outputs;wavelet denoising a first subset of filtered PCG outputs of theplurality of filtered PCG outputs to form a M number of denoisedfiltered PCG outputs, where M is equal to L multiply by K; transforming,utilizing an Independent-Component-Analysis (ICA), a second subset offiltered PCG outputs of the plurality of filtered PCG outputs to formthe M number of filtered ICA transforms; transforming, utilizing theIndependent-Component-Analysis (ICA), a first portion of the secondsubset of denoised filtered PCG outputs to form the M number of denoisedfiltered ICA transforms; compiling a S number of a plurality ofdetection heartbeat (DH) inputs, including: i) the M number of filteredPCG outputs, ii) the M number of denoised filtered PCG outputs, iii) theM number of filtered ICA transforms, and iv) the M number of denoisedfiltered ICA transforms; detecting beat locations of beats in each of DHinputs; calculating a confidence score that describes a probability thatthe beats in each DH input of the plurality of DH inputs representactual heartbeats and not a noise; dividing the plurality of DH inputsinto at least two groups: i) a first group of DH inputs containing fetalheartbeats, ii) a second group of DH inputs containing maternalheartbeats; selecting, from the first group of DH inputs, at least oneparticular fetal DH input that contains the fetal heartbeat based on afirst confidence score of the at least one particular fetal DH input;and selecting, from the second group of DH inputs, at least oneparticular maternal DH input that contains the maternal heartbeat, basedon a second confidence score of the at least one particular maternal DHinput.

Estimating Fetal Heart Rate Over a Particular Time Interval According toSome Embodiments of the Present Invention

Referring to FIG. 45, a graphical representation is shown where the ECGsignals data has “good” signal properties (i.e. is a signal from whichfetal heart beats can be detected), but the PCG signals data has “poor”signal properties. Similarly, referring to FIG. 46, a graphicalrepresentation is shown where the PCG signals data has “good” signalproperties, but the ECG signals data is “poor” shows a graphical displaywhere the ECG signals data has “good” signal properties, but the PCGsignals data is “poor”.

It is an objective of the present invention to combine the ECG signalsdata and the PCG signals data to increase the accuracy of the system ofthe present invention to consolidate ECG signals data and PCG signalsdata recorded over a particular time interval to calculate an estimationof fetal heart rate over the particular time interval.

In some embodiments, the present invention provides a system forgenerating an estimation of fetal cardiac activity over a particulartime interval comprising:

-   -   a) a specifically programmed computer system comprising: at        least one specialized computer machine, comprising: a        non-transient memory, electronically storing particular computer        executable program code; and at least one computer processor        which, when executing the particular program code, becomes a        specifically programmed computing processor that is configured        to at least perform the following operations:        -   i. receiving a calculated fetal heart rate for a plurality            of time points over a particular time interval from filtered            N-ECG fetal signals data and a calculated fetal heart rate            for a plurality of time points over a particular time            interval from filtered PCG outputs;        -   ii. determining the score of the calculated fetal heart rate            for the plurality of time points over the particular time            interval for the filtered N-ECG fetal signals;        -   iii. determining the score of the calculated fetal heart            rate for the plurality of time points over the particular            time interval for the filtered PCG outputs;        -   iv. based on the calculated fetal heart rate and score for a            plurality of time points over a particular time interval            from filtered N-ECG fetal signals data, and the calculated            fetal heart rate and score for a plurality of time points            over a particular time interval from filtered PCG outputs,            determining a consolidated fetal heart rate and score for            the plurality of time points over the particular time            interval,            -   wherein the consolidated fetal heart rate and score for                an individual time point within the plurality of time                points is determined as one of the four options selected                from the group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;        -   v. based on the consolidated heart rate and scores for the            plurality of time points over the particular time interval,            generating, by the at least one computer processor, a fetal            heart rate probability mesh;        -   vi. based on the fetal heart rate probability mesh,            generating, by the at least one computer processor, an            estimated fetal heart rate over the particular time            interval,            -   wherein the estimated fetal heart rate over the                particular time interval is calculated based on (1) cost                representing fetal heart probability mesh values at each                point of the estimated fetal heart rate over the                particular time interval; and (2) cost representing the                overall tortuosity of the estimated fetal heart rate                over the particular time interval.

In some embodiments, referring to FIG. 32, fetal hear rate is calculatedfiltered N-ECG signals data and the filtered PCG outputs using the RRinterval of the fetal cardiac activity cycle.

In some embodiments, the present invention provides acomputer-implemented method, that includes:

-   -   a) receiving a calculated fetal heart rate for a plurality of        time points over a particular time interval from filtered N-ECG        fetal signals data and a calculated fetal heart rate for a        plurality of time points over a particular time interval from        filtered PCG outputs;    -   b) determining the score of the calculated fetal heart rate for        the plurality of time points over the particular time interval        for the filtered N-ECG fetal signals;    -   c) determining the score of the calculated fetal heart rate for        the plurality of time points over the particular time interval        for the filtered PCG outputs;    -   d) based on the calculated fetal heart rate and score for a        plurality of time points over a particular time interval from        filtered N-ECG fetal signals data, and the calculated fetal        heart rate and score for a plurality of time points over a        particular time interval from filtered PCG outputs, determining        a consolidated fetal heart rate and score for the plurality of        time points over the particular time interval,        -   wherein the consolidated fetal heart rate and score for an            individual time point within the plurality of time points is            determined as one of the four options selected from the            group consisting of:            -   1. the weighted average of the calculated heart rate                from the filtered N-ECG fetal signals data and the                filtered PCG outputs for the individual time point, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by 10 beats per minute or                less, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   2. the calculated heart rate having the lower score, if                the calculated heart rate from the filtered N-ECG fetal                signals data and the filtered PCG outputs for the                individual time point differs by more than 10 beats per                minute, and if the scores of the calculated fetal heart                rate for the individual time point for both the filtered                N-ECG fetal signals data and the filtered PCG outputs                are valid;            -   3. the calculated heart rate that has the valid score;                and            -   4. no consolidated fetal heart rate and score, if                neither the calculated heart rate from the filtered                N-ECG fetal signals data or the filtered PCG outputs has                a valid score;    -   e) based on the consolidated heart rate and scores for the        plurality of time points over the particular time interval,        generating, by the at least one computer processor, a fetal        heart rate probability mesh;    -   f) based on the fetal heart rate probability mesh, generating,        by the at least one computer processor, an estimated fetal heart        rate over the particular time interval,        -   wherein the estimated fetal heart rate over the particular            time interval is calculated based on (1) cost representing            fetal heart probability mesh values at each point of the            estimated fetal heart rate over the particular time            interval; and (2) cost representing the overall tortuosity            of the estimated fetal heart rate over the particular time            interval.

In some embodiments, the particular time interval is 120 seconds. Insome embodiments, the particular time interval is 110 seconds. In someembodiments, the particular time interval is 100 seconds. In someembodiments, the particular time interval is 90 seconds. In someembodiments, the particular time interval is 80 seconds. In someembodiments, the particular time interval is 70 seconds. In someembodiments, the particular time interval is 60 seconds. In someembodiments, the particular time interval is 50 seconds. In someembodiments, the particular time interval is 40 seconds. In someembodiments, the particular time interval is 30 seconds. In someembodiments, the particular time interval is 20 seconds. In someembodiments, the particular time interval is 10 seconds.

In some embodiments, data is acquired every five seconds within theparticular time interval. In some embodiments, data is acquired everyfour seconds within the particular time interval. In some embodiments,data is acquired every three seconds within the particular timeinterval. In some embodiments, data is acquired every two seconds withinthe particular time interval. In some embodiments, data is acquiredevery one second within the particular time interval.

In some embodiments, referring to FIGS. 47 to 48, the specificallyprogrammed computer system determines consolidated fetal heart rateestimations and scores for individual time points within the particulartime period wherein the consolidated fetal heart rate and score for anindividual time point within the plurality of time points is determinedas one of the four options selected from the group consisting of:

-   -   1. the weighted average of the calculated heart rate from the        filtered N-ECG fetal signals data and the filtered PCG outputs        for the individual time point, if the calculated heart rate from        the filtered N-ECG fetal signals data and the filtered PCG        outputs for the individual time point differs by 10 beats per        minute or less, and if the scores of the calculated fetal heart        rate for the individual time point for both the filtered N-ECG        fetal signals data and the filtered PCG outputs are valid;    -   2. the calculated heart rate having the lower score, if the        calculated heart rate from the filtered N-ECG fetal signals data        and the filtered PCG outputs for the individual time point        differs by more than 10 beats per minute, and if the scores of        the calculated fetal heart rate for the individual time point        for both the filtered N-ECG fetal signals data and the filtered        PCG outputs are valid;    -   3. the calculated heart rate that has the valid score; and    -   4. no consolidated fetal heart rate and score, if neither the        calculated heart rate from the filtered N-ECG fetal signals data        or the filtered PCG outputs has a valid score.

In some embodiments, the weighted average, is determined as follows:

${( \frac{{Acoustic}_{Score}}{{ECG}_{Score} + {Acoustic}_{Score}} ) \cdot {ECG}_{HR}} + {( \frac{{ECG}_{Score}}{{ECG}_{Score} + {Acoustic}_{Score}} ) \cdot {Acoustic}_{HR}}$

In some embodiments, the score for the consolidated fetal heart rateestimation above is as follows:

${( \frac{{Acoustic}_{Score}}{{ECG}_{Score} + {Acoustic}_{Score}} ) \cdot {ECG}_{Score}} + {( \frac{{ECG}_{Score}}{{ECG}_{Score} + {Acoustic}_{Score}} ) \cdot {Acoustic}_{Score}}$

In some embodiments, the calculated heart rate from the filtered N-ECGfetal signals data or the filtered PCG outputs for the individual timepoint is valid, if the score of the calculated heart rate from thefiltered N-ECG fetal signals data or the filtered PCG outputs is notgreater than 0.15. Alternatively, the calculated heart rate from thefiltered N-ECG fetal signals data or the filtered PCG outputs for theindividual time point is valid, if the score of the calculated heartrate from the filtered N-ECG fetal signals data or the filtered PCGoutputs is not greater than 0.2. Alternatively, the calculated heartrate from the filtered N-ECG fetal signals data or the filtered PCGoutputs for the individual time point is valid, if the score of thecalculated heart rate from the filtered N-ECG fetal signals data or thefiltered PCG outputs is not greater than 0.10. Alternatively, thecalculated heart rate from the filtered N-ECG fetal signals data or thefiltered PCG outputs for the individual time point is valid, if thescore of the calculated heart rate from the filtered N-ECG fetal signalsdata or the filtered PCG outputs is not greater than 0.05.

Alternatively, in some embodiments, the at least one computer processor,instead of generating a set of consolidated fetal heart rare estimationsand scores, uses either the heart rate determined by either the filteredN-ECG fetal signals data or the filtered PCG outputs.

In some embodiments, a fetal heart rate probability mesh is generatedfrom the consolidated fetal heart rate estimations and scores. In someembodiments, the fetal heart rate probability function for eachindividual time point within the plurality of time points is modeled asa flipped normalized probability density function. In some embodiments,the fetal heart rate probability function is calculated as follows:

-   -   1. Defined for all possible fetal heart rate values ranging from        60 to 180 beats per minute.        -   2. Location of minimum is set to be the fetal heart rate            estimation.        -   3. Value of minimum is calculated according to score (better            score translates to a deeper peak).        -   4. Width of function is calculated according to score            (better score translates to narrower probability function).

An example of a fetal heart rate probability mesh according to someembodiments of the present invention is shown in FIGS. 49 and 50.

In some embodiments, an estimation of the fetal heart rate over theentire particular period of time is generated by the specificallyprogrammed computer, using the fetal heart rate probability mesh.

In some embodiments, estimating the fetal heart rate over the particulartime interval is achieved by calculating an estimation with minimalaccumulated cost; wherein the minimal accumulated cost is calculatedbased on (1) cost representing fetal heart probability mesh values ateach point of the estimated fetal heart rate over the particular timeinterval; and (2) cost representing the overall tortuosity of theestimated fetal heart rate over the particular time interval.

In some embodiments, instead of estimating the fetal heart rate over theparticular time interval by calculating an estimation with minimalaccumulated cost, the specifically programmed computer generates anestimated fetal heart rate over the particular time interval using theconsolidated fetal hear rate estimations.

In some embodiments, estimating the fetal heart rate over the particulartime interval is achieved by calculating an estimation with minimalaccumulated cost, wherein, using dynamic programming, an accumulatedcost mesh is constructed, where each value of the accumulated cost meshis a sum of the fetal heart rate probability mesh value at that pointand of the minimal path in its neighborhood in the previous step:

E(i,j)=e(i,j)+min(E(i−1,j−k)); k=−4:4

where:

-   -   e is the value of the fetal heart rate probability mesh.    -   E is the accumulated cost.    -   i represents time, j represents heart rate values in        neighborhood of +/−4 [bpm/second].

FIGS. 52 and 53 show a fetal heart rate estimation probability mesh, andan accumulated cost mesh built from the fetal heart rate estimationprobability mesh, respectively. Referring to FIGS. 52 and 53 as anillustrative example, in some embodiments, the minimal path is found byfinding the minimal value of all possible paths, which is, in theillustrative example, at the right column of the table, at Time=56seconds. Minimal value is 10.9652, at HR=120 bpm. The minimal path isthen selected by moving back in time, choosing the minimal value at eachprevious step in time in the range of +/−20 bpm/second, alternatively inthe range of +/−10 bpm/second, alternatively in the range of +/−9bpm/second, alternatively in the range of +/−8 bpm/second, alternativelyin the range of +/−7 bpm/second, alternatively in the range of +/−6bpm/second, alternatively in the range of +/−5 bpm/second, alternativelyin the range of +/−4 bpm/second, alternatively in the range of +/−3bpm/second, alternatively in the range of +/−2 bpm/second, alternativelyin the range of +/−1 beats per minute.

In an alternate embodiment, the heart rate in the mesh range from 60-200beats per minute, in 1 bpm increments.

Alternatively, the minimal path is computed using an exhaustive search,computing all possible paths in the fetal heart rate probability mesh.

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in anon-limiting fashion.

EXAMPLES Example 1: Electrodes According to Some Embodiments of thePresent Invention

Various electrodes were manufactured according to the embodiment shownin FIG. 5 and evaluated. The following parameters were tested: thesurface resistance/resistivity (MSSR); basic transfer function testing(BTFT); bio-parameters (PhysioPM); and real-life recordings of fetalcardiac electrical signals (RLPysioPM). Table 1 summarizes theelectrodes tested.

TABLE 1 Measured* Surface Surface Available conductivity conductivitysize Serial# ID [Ohm/Sq] [Ohm/Sq] [cm × cm] Materials Notes 1 Orange_IT<2 3.5 20*20 Silver Anisotropic, Strechable 2 C+ <4 4 70*70 SilverAnisotropic, Strechable 3 Shaoxing17 — 0.3 1 m{circumflex over ( )}2Silver Isotropic, Non-strechable 4 Shaoxing27 — 0.6 1 m{circumflex over( )}2 Silver Isotropic, Non-strechable 5 Tech P130 + B <5 1.1 40*40Silver Isotropic, Strechable 6 Silver30 — 3.5 20*15 Silver Isotropic,Strechable

FIG. 9 shows a micrograph of electrically conductive fabric usedelectrode serial no. 1. FIG. 10 shows a micrograph of electricallyconductive fabric used electrode serial no. 2. FIG. 11 shows amicrograph of electrically conductive fabric used electrode serial no.3. FIG. 12 shows a micrograph of electrically conductive fabric usedelectrode serial no. 4. FIG. 13 shows a micrograph of electricallyconductive fabric used electrode serial no. 5. FIG. 14 shows amicrograph of electrically conductive fabric used electrode serial no.6.

Table 2 a-f shows the MSSR values observed from the electrodes tested.Table 3 shows the observed anisotropy of the electrodes tested.

TABLE 2a Sens 1 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 0.243 0.245 0.246 0.245 0.230 CD AB 1 0.228 0.227 0.2260.227 BA DC 1 0.224 0.225 0.222 0.224 DC BA 1 0.227 0.224 0.222 0.224 BCDA 1 0.489 0.487 0.484 0.487 0.459 DA BC 0 0 0 0 0.000 CB AD 1 0.4440.443 0.441 0.443 AD CB 1 0.45 0.448 0.446 0.448

TABLE 2b Sens 2 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 1.022 1.02 1.014 1.019 1.032 CD AB 1 0.999 0.996 0.9950.997 BA DC 1 0.994 0.984 0.979 0.986 DC BA 1 1.13 1.123 1.123 1.125 BCDA 0 0 0 0 0.000 NaN Main DA BC 0 0 0 0 0.000 direc- CB AD 0 0 0 0 0.000tion AD CB 0 0 0 0 0.000

TABLE 2c Sens 3 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 0.039 0.038 0.04 0.039 0.031 CD AB 1 0.027 0.024 0.0250.025 BA DC 1 0.023 0.023 0.023 0.023 DC BA 1 0.036 0.035 0.035 0.035 BCDA 1 0.026 0.028 0.026 0.027 0.026 DA BC 1 0.024 0.025 0.023 0.024 CB AD1 0.027 0.026 0.026 0.026 AD CB 1 0.027 0.024 0.026 0.026

TABLE 2d Sens 4 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 0.026 0.063 0.064 0.051 0.058 CD AB 1 0.058 0.057 0.0590.058 BA DC 1 0.06 0.059 0.058 0.059 DC BA 1 0.066 0.065 0.066 0.066 BCDA 1 0.045 0.045 0.045 0.045 0.044 DA BC 1 0.043 0.044 0.043 0.043 CB AD1 0.045 0.043 0.043 0.044 AD CB 1 0.044 0.042 0.043 0.043

TABLE 2e Sens 5 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 0.222 0.224 0.223 0.223 0.194 CD AB 1 0.212 0.211 0.2130.212 BA DC 1 0.22 0.219 0.222 0.220 DC BA 1 0.233 0.065 0.066 0.121 BCDA 1 0.074 0.073 0.073 0.073 0.075 DA BC 1 0.083 0.084 0.084 0.084 CB AD1 0.074 0.075 0.075 0.075 AD CB 1 0.065 0.067 0.067 0.086

TABLE 2f Sens 6 AVG Aniso- Inject Measure Check? M1 M2 M3 [Ω] tropicNotes AB CD 1 0.05 0.048 0.049 0.049 0.046 CD AB 1 0.038 0.039 0.0370.038 BA DC 1 0.055 0.057 0.056 0.056 DC BA 1 0.043 0.041 0.042 0.042 BCDA 1 0.911 0.903 0.898 0.904 0.898 DA BC 1 0.904 0.891 0.897 0.897 CB AD1 0.885 0.886 0.882 0.354 AD CB 1 0.903 0.903 0.908 0.905

TABLE 3 Main 2nd ID direction direction Anisotropy 1 0.230 0.459 50% 20.000* 1.032 100%  3 0.026 0.031 16% 4 0.044 0.058 25% 5 0.075 0.194 62%6 0.046 0.898 95%

The impedance between the fabric and the lead connector was alsodetermined. The electrodes were connected to a copper sheet, and apressure of 34.386 kPa was applied, using a 1.01026 kg weight. Themeasured impedance of the measuring system was 0.109Ω, and this valuewas subtracted from the measured impedance of the electrodes. Theresults are shown in Table 4. Electrode serial no. 5 was observed tohave the greatest surface area in contact with the skin.

TABLE 4 AVG Value ID Check? M1 M2 M3 [Ω] [Ω] 1 1 0.694 0.685 0.682 0.6870.578 2 1 0.461 0.460 0.452 0.458 0.349 3 1 0.206 0.205 0.206 0.2060.097 4 1 0.271 0.259 0.268 0.269 0.160 5 1 0.309 0.307 0.308 0.3080.199 6 1 0.709 0.662 0.664 0.678 0.569

Electrodes 3-5 performed best. Performance was scored as follows:

Req. Test ID Category Category Details 0.1 Performance General RecordECG signals 1.1 Performance MSRR Surface resistivity below 1 [Ω · m] 1.2Performance MSRR Surface resistance below 1 [Ω/sq] 2.1 Performance BTFTSINAD is higher than 50 dB 2.2 Performance BTFT SNR is higher than 50 dB2.3 Performance BTFT CORR COEF higher than 0.95 3.1 Performance PysioPMSkin-Sensor impedance below 0.15 [MΩ] 3.2 Performance PysioPM Self-noiseof the sensor below 0.1 μV 3.3 Performance PysioPM Immunity to motionartifacts 3.4 Performance PysioPM Power-line noise rejection higher than80 dB 4.1 Performance RLPysioPM Fetal ECG is visible in more than 1record 4.2 Performance RLPysioPM Fetal ECG SNR is higher than 1 dB 3.5Performance RLPysioPM Relative fetal ECG SNR (relative to the referencesensors) is higher than 0.85

A summary of the MSSR results for electrodes 3-5 is shown in Table 5.The fabric of electrode serial no. 6 was weak, and has large voidsbetween the fibers (see FIG. 14) and was therefore unsuitable. Electrodeserial no. 2 was excluded because the surface resistivity was greaterthan 1 Ω/square.

TABLE 5 Surface resistance Main 2nd Aniso- ID direction direction tropyS2C Notes 3 0.026 0.031 0.163 0.097 Lowest resistance, non-stretchable,Isotropic 4 0.044 0.058 0.251 0.160 Mid 5 0.075 0.194 0.616 0.199 Lowresistance, Highest Anisotropy, highly stretchable

BTFT Results: BTFT measurements were obtained using the methodsdescribed in Example 3 below. Table 6 shows the results.

TABLE 6 Relative diff RMS SINAD SNR ID CORRCOEF CORRLAG [%] Input OutputRel RelRef Input Output Rel Ref 1 0 0.012 48.545 48.541 0.008% 0.000%53.852 53.836 0.029% 1 1 0 0.010 47.964 47.956 0.015% 1.205% 54.27854.242 0.066% 2 1 0 0.016 48.088 48.077 0.021% 0.955% 54.173 54.1360.068% 3 1 0 0.010 48.231 48.222 0.018% 0.657% 54.197 54.161 0.067% 4 10 0.015 48.303 48.293 0.020% 0.511% 54.042 54.006 0.066% 5 1 0 0.0154S.527 48.517 0.020% 0.049% 54.137 54.102 0.064% 6 1 0 0.014 48.60648.597 0.018% 0.115% 54.113 54.073 0.074%

CORRCOEF: is the linear correlation coefficient between the input andthe output signals; CORRLAG: is the lag between the input and outputsignals; Relative diff RMS: is the relative difference in the RMS of theinput and output signals in %; SINAD.Rel: is the relative percentagedifference in the SINAD values between the input and the output signals;SINAD.RelRef: is the relative percentage difference in the signal tonoise and distortion ratio (SINAD) values between the output signal andthe reference signal; SNR.Rel: is the relative percentage difference inthe SNR values between the input and the output signals. The BTFTresults show that the electrodes that have the best performance in termsof SNR and relative SINAD is electrode serial no. 5 followed byelectrode serial no. 4, then electrode serial no. 3.

PysioPM: PysioPM measurements were obtained according to the methodsdescribed in Example 4. Table 7 shows the results of the measuredimpedance.

TABLE 7 ID Sens1 Sens2 Sens3 Sens4 MAXDIFF 3 0.733 0.667 0.651 0.75413.66% 4 1.396 1.495 1.281 1.503 14.77% 5 4.251 3.1 3.551 3.921 27.08%

The values observed include the resistance of a 5 cm lead wire, thecopper sheet, and a cable connected to the copper sheet.

Impedance of the Interface between the electrode and the skin: Theimpedance was measured between 2 electrodes placed on skin 20 mm apart.Table 8 shows the average of 3 experiments.

TABLE 8 Average ID Bioimpedance [MΩ] 3 0.602 4 0.227 5 0.135

Recorded ECG Signals Data using the Electrodes: FIG. 14, panels a-c showrecorded ECG signals data using electrode serial nos. 3-5 respectively.Electrodes 3-5 were able to filter out powerline noise, and had similaramplitudes. However, all electrodes were susceptible to movementartifacts.

ECG signal were recorded from two pregnant subjects at week 25 and week28, using eitherelectrodes 3, 4, 5, and a comparison electrode, using awet contact electrode, using the electrode position B1-B3 (see FIG. 29for the electrode position). FIG. 16, panels a-d, and FIG. 17, panelsa-d show the recorded ECG signals data using electrode serial nos. 3-5,and the GE comparison electrode respectively at 25 weeks in the twosubjects. Fetal ECG were visible in the traces.

Example 2: Measuring Surface Resistivity and Resistance

FIG. 18 shows an experimental set up to determine surface resistivityand resistance of an electrically conductive fabric according to someembodiments of the present invention. A, B, C, and D are point contactconnectors. To measure surface resistivity, current was introduced andrecorded according to the following protocol:

1. Connect the sample as described in the background section.

2. Make sure that the current source is running and stable.

3. Inject AB, measure CD;

4. Inject CD, measure AB;

5. Inject BA, measure DC;

6. Inject DC, measure BA;

7. Inject BC, measure DA;

8. Inject DA, measure BC;

9. Inject CB, measure AD;

10. Inject AD, measure CB;

Surface resistance was calculated according to the following:

${{\exp ( {- \frac{\pi \; R_{{AB},{CD}}}{R_{s}}} )} + {\exp ( {- \frac{\pi \; R_{{BC},{AD}}}{R_{s}}} )}} = 1$${{where}\mspace{14mu} {R_{{AB},{CD}}\lbrack\Omega\rbrack}} = {\frac{V_{DC}}{i_{AB}} = \frac{V_{D}V_{C}}{i_{AB}}}$

is the resistance measured between C and D while introducing currentbetween points A and B; and i_(AB)[A] is the injected current betweenpoints A and B; and d [m] is the thickness of the sample; and ρ is theresistivity.

ρ = R_(s)d[Ω ⋅ m]$R_{s} = {\frac{\pi}{\ln \mspace{11mu} 2} \cdot R}$R = R_(vertical) = R_(horizontal)$R_{vertical} = \frac{( {R_{{AB},{CD}} + R_{{CD},{AB}} + R_{{BA},{DC}} + R_{{DC},{BA}}} )}{2}$$R_{horizontal} = \frac{( {R_{{BC},{DA}} + R_{{DA},{BC}} + R_{{CB},{AD}} + R_{{AD},{CB}}} )}{2}$

The above protocol was performed using an electrode alone, or anelectrode contacting a copper sheet (to measure the resistivity of theelectrode-surface interface). Additionally, measurements were obtainedafter the electrically conductive fabric was stretched either 20%, or50% in the man direction, or in the direction perpendicular to the maindirection.

Example 3: Basic Transfer Function Testing

An electrode was placed on a copper sheet, such that the cutaneouscontact is in contact with the copper sheet, and a 1 kg mass was appliedto the electrode. The copper sheet was connected to the positiveterminal of a signal generator, the electrode was connected to thepositive input of an amplifier, and the other input of the amplifier wasconnected to ground. FIG. 19 shows the experimental setup describedabove. A 30 Hz signal was generated by the signal amplifier, and thefollowing parameters were recorded:

1. Time domain:

-   -   a. amplitude-2-amplitude; and    -   b. non-zero division; and    -   c. time shifts; and    -   d. cross correlation; and    -   e. correlation coefficient; and    -   f. Histogram: Mean, RMS, STD.

2. Frequency domain:

-   -   a. Welch PSD estimation (magnitude); and    -   b. Cross coherence; and    -   c. Main frequency magnitude; and    -   d. Dominant frequencies magnitude; and    -   e. SINAD, SNR.

Example 4: Electrophysiological Performance Measurements

The source of the physiological signals detected using the electrodesaccording to some embodiments of the present invention are locatedwithin the body of the pregnant human subject and have extremely lowamplitude and low frequency. Without intending to be limited by anyparticular theory, the physiological signals flow within the body of thepregnant human subject by the movement of ions. The electrodes accordingto some embodiments of the present invention act as signal transducers,and transduce the movement of ions to the movements of electrons. Theskin-electrode interface (SSI) is one determining factor of theelectrode's ability to transduce the physiological signals.

The SSI for the electrodes according to some embodiments of the presentinvention may be modeled by a parallel circuit of an ohmic andcapacitive impedance with an additional Warburg resistance (see FIG.20). Without intending to be limited to any particular theory, both theconductive and the capacitive compartments affect the performance of anelectrode according to some embodiments of the present invention. Theskin-electroce impedance (SSiM) is equivalent to the impedance of thecircuit shown in FIG. 20, and ranges from 10 kΩ to 100 MΩ. Decreasingthe impedance improves the performance of an electrode according to someembodiments of the present invention. Decreasing impedance may beachieved by increasing the surface areas of the cutaneous contact, or byreducing the resistivity of the cuntaneous contact. An increase in inputimpedance and a decrease in input capacitance of the amplifier may alsoimproves the performance of an electrode according to some embodimentsof the present invention.

In the test protocol, electrodes were applied to the skin of a subject'shand, according to the arrangement shown in FIG. 21. The surface of thehand having first been cleaned. Four VELCRO straps were applied, thepressure of the straps was confirmed to be equal, using a surfacepressure sensor. Test electrodes were then inserted under the straps.The pressure that the electrodes contact the skin was confirmed to beequal, using a surface pressure sensor. Impedance was measured asfollows:

-   -   2-wire: measure the 2wire resistance between the S_(i)        electrodes and the S_(o) electrodes (2 measurements).    -   4-wire: use the S_(i) electrodes as the injectors and the S_(o)        electrodes as the measurers. Measure the resistance (1        measurement).    -   Capacitance measurement: measure the 2wire capacitance between        the S_(i) electrodes and the S_(o) electrodes (2 measurements).

A 150 mV_(pp) sine wave was applied to the S_(i) electrodes, and thevoltage developed at the S_(o) electrodes was recorded using a BioPacamplifier. Recordings were obtained using a sine wave of the followingfrequencies: 0.1, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70,80, 90, Hz.

Example 4: Algorithm Design According to Some Embodiments of the PresentInvention

TABLE 9 5. Low Level Design of the algorithm: The fECG detectionalgorithm have the following LLD: 5.1. The acquired N-channels signalsshould be preprocessed: 5.1.1. Signal exclusion: Saturated signals areexcluded in this version (if the saturated part exceeds 10% of overalllength of the signal). For the next versions, this criterion should bereplaced by an online examiner. 5.1.2. Baseline removal: the baseline ofthe ECG signal needs to be removed. It can be removed using a number ofmethods. In the algorithm the baseline is removed using a moving averagefilter with an order of 501 (milliseconds). 5.1.3. Low pass filteringwith an IIR filter (auto designed by Matlab): 5.1.3.1. Type: ChebyshevType 1 5.1.3.2. Cutoff freq: 70Hz 5.1.3.3. Baseband ripple: 0.12dB5.1.3.4. Order: 12th order,6 sections 5.1.4. Power line interferencecancelation: to be in the safe side and for simplicity purposes, thefollowing filter is used: 5.1.4.1. Type: Butterworth (band - stop)5.1.4.2. Cuttoff freq: 49.5 - 50.5Hz 5.1.4.3. Order: 10th order,5sections 5.2. Maternal ECG detection: The following steps are performed:5.2.1. Examination: the data is passed through an additional high passfilter (5th order @1Hz). The data is examined by preliminary and simplepeak detector. The relative energy of the peaks is calculated (relativeto the overall energy of the signal). Each signal is given a qualityscore depending on this measure. Signals with a quality score of lessthan a threshold are excluded. In addition, the signals are examined formissing data and NaNs. 5.2.2. Peak enhancement: The preprocessed ECGdata is passed through an additional median filter with an order of 100samples to enhance the maternal peaks. 5.2.3. Peaks detection: for eachsignal: 5.2.3.1. Divide the signal into 10 seconds segments, for eachsegment:

TABLE 10 5.2.3.2. Normalize the data 5.2.3.3. Find the local peaks inthe segment using derivative, thresholding and minimum distances methods5.2.3.4. After finishing with all of the segments perform kmedoidsclustering on the number of peaks in each segments: 5.2.3.4.1. Forsegments with very low number of peaks, (apparently there is a noisespike) perform AGC on the data and redetect the peaks. If the new numberof peaks belongs to one of the good clusters, add these peaks. If not,ignore these peaks 5.2.3.4.2. For segments with very high number ofpeaks (due to high noise most probably), ignore these peaks 5.2.3.5.After finishing, perform kmedoids clustering on the amplitudes of thepeaks, very low peaks and very high peaks are filtered using this step.5.2.3.6. After finishing with the signal, do the following: 5.2.3.6.1.Check that the number of peaks falls into an expected interval (based onthe maximum and minimum possible heart rates) 5.2.3.6.2. Is step 1 ispassed, check that the relative energy of the QRS complexes is higherthan a threshold 5.2.3.6.3. If step 2 is passed, perform peakredetection using the cross correlation method (helps removing falsepositive and adding false negatives) 5.2.4. Peak re-detection: if theprevious step is failed to detect the peaks in at least one channel,apply ICA to the data and then repeat step (5.2.3) 5.2.5. Peaksexamination: 5.2.5.1. A score is given for each one of the N signals.The best lead is defined as the lead with the biggest score 5.2.5.2. Aglobal peak array is constructed based on the best lead with correctionsmade using the peaks from the other leads

TABLE 11 5.5.5.3. The global peaks array is examined using physiologicalmeasures (RR intervals, HR, HRV) 5.2.6. HRC calculation: the mHRC iscalculated using the detected maternal R-waves 5.3. Maternal ECGElimination: The purpose of this step is to eliminate the maternal ECGand thereafter stay with the fetal ECG signals and some noise accordingto the model that have been previously developed. The following stepsare performed for each of the N-channels. 5.3.1. Resample the signal tomatch a 4kSPS sampling frequency 5.3.2. Find the QRS onset and offsetpositions using the Curve Length Transform (CLT) 5.3.3. For each one ofthe detected maternal peaks: 5.3.3.1. Define a beat interval: the beatinterval is defined as, except for the first and last peaks, (1) bestonset: half the distance between the current peak and the previous peakand (2) beat offset: half the distance between the current peak and thenext peak. Due to the beat-2-beat changing heart rate, the beat intervalshould change for each beat 5.3.3.2. Define a local template, a templatethat depends on the current beat: 5.3.3.2.1. Step 1: Search for at least10 beats that have a correlation coefficient (obtained from crosscorrelation) of at least 0.98 This step is usually fast and givesresults for not-noisy signals 5.3.3.2.2. Step 2: Step 1 usually failsfor noisy signals hence an interative scheme is used: 5.3.3.2.2.1.Define the template as the beat itself 5.3.3.2.2.2. An initialcorrelation coefficient is defined (high value threshold: 0.99)5.3.3.2.2.3. Search for the beats that are very similar to the currentbeat (have higher correlation than the threshold) 5.3.3.2.2.4. If noneis found, decrease the correlation threshold and repeat

TABLE 12 5.3.3.2.2.5. If some is found, update the template and continue5.3.3.2.2.6. The procedure is terminated if: (1) a maximum number ofiterations is reached, (2) the number of beats to include reached aminimum number or (3) the current iteration yields the same results asthe previous iteration 5.3.3.2.3. A byproduct of this process is a scorefor how noisy each beat is 5.3.4. Start the adaptation procedure,iterative Levenberg-Marquardt Parametric Optimization (LMPO) for solvingnon-linear damped least squares problems. Divide the beat into 3 parts:(1) P-wave region, (2) QRS complex and (3) T-wave region. The iterativeprocedure: 5.3.4.1. Initialize the LMA algorithm (Algorithm params)5.3.4.2. Perform initial guess for the mECG (first guess is thetemplate, let it be ϕ_c) 5.3.4.3. Adapt the initial guess 5.3.4.4.Compare the results to the measured current ECG (current beat withmaternal data, fetal data and noise, let it be ϕ_m) 5.3.4.5. Check if atermination criteria is reached, if yes terminate returning the lastgood result 5.3.4.6. If not, update the algorithm parameters and repeatsteps 3-5. 5.3.4.7. This algorithm has the following characteristics: Itinterpolates between Gauss - Newton (GN) method and the Gradient Decent(GD) method taking the good form both At first, it convergence very fastdue to the fast convergence of GD. When it advances, the steps becomesmaller, yielding a slow convergence when it is close to the localminimum (Slow yet cautious progress near the minimum!)

TABLE 13 It operates by the principle of “worst case scenario is to staythe same” The only problem with this iterative algorithm is theconvergence to local minima. This isn't a big issue in our case since weknow that the local minima is also, always, the global one (due to theeducated guess of the start conditions) The algorithm minimizes theerror energy function defined as the l2 norm of the difference betweenthe calculated potentials and the measured potentials: E= lϕ_c−ϕ_ml{circumflex over ( )}2 The final result of the algorithm is a stable,global and reproducible solution due to the fact the number ofparameters to be reconstructed is way smaller than the number of theavailable observations The reconstruction parameters of the algorithmare: P region multiplier QRS region multiplier T region multiplier Beatshift

TABLE 14 5.3.5. Construct the maternal ECG (mECG) array (started formthe templates for each beat and get close to the current channelmeasured ECG) 5.3.6. Construct the fetal ECG array, fECG=ECH−mECG 5.3.7.Downsample the mECG and teh fECG signals to match the initial samplingrate 5.4. Fetal ECG preprocessing: After the elimination (near-perfectelimination) of the mECG, the remaining data is processed for fECGenhancement (see the next step for more info). A preliminary predictionof the best fetal channel is performed depending on the energydistribution of the auto correlation function of the signal 5.5. FetalECG detection: 5.5.1. First step is to prepare the data. Usually, thefetal signals do not appear in all of the channels hence it is beter todecrease to dimensions of the data. This is done by: 5.5.2. ApplySingular Value Decomposition (SVD) keeping only the N-1 singular valuesand vectors 5.5.3. Apply fastICA to the resulting data (with ‘tauh’)5.5.4. Apply peack-2-mean transformation. 5.5.4.1. Use a moving windowto calculate the relation: max{signal[win|}/mean(signal[win|}(enhancespeaks) 5.5.4.2. Calculate the 1st derivative of the result of step 15.5.4.3. Find the zero crossing the find peaks, take the negative partof the derivative 5.5.4.4. Find the peaks in the previous result andcluster them based on the difference between them. Identify the bestgroup as the group with the maximum score: SCR=n/RMS where n is thenumber of peaks in each cluster and RMS is the energy of the derivativeof the distance between the peaks 5.5.4.5.Perform a prediction for theRR interval for the best group 5.5.4.6. If the prediction doesn't fitthe real model, ignore this channel

TABLE 15 5.5.4.7. If so, calculate the auto correlation function of awindowed RMS of the signal, if the result have a narrow distributionignore this channel, else: 5.5.4.8. Apply AGC to the negative derivativesignal 5.5.4.9. Normalize the result, and flag it as the processing data5.5.4.10.Repeat steps 1-10 to the N-channels 5.5.5. Try to perform fetalpeak detection using the same methodology as for the maternal peakdetection with different parameters 5.5.6. If the previous step issuccessful, skip this step. If not, this means that the fetal data isnoisy. In this case perform the following preprocessing: 5.5.6.1. Thefetal data is band-pass filtered between the range 15-70Hz 5.5.6.2.Wavelet denoising is applied to the data 5.5.6.3. Windowed RMS isapplied to the data 5.5.6.4. The results are low pass filtered @35Hz5.5.6.5. The data is normalized and AGC is applied to the data 5.5.7.Following step (5.5.6), perform fetal peak detection without thecorrelation part 5.5.8. Following step (5.5.7), if more than one channelis valid, calculate the kurtosis of the distribution of the distancebetween the peaks. The best lead is defined as the peak with the maximumrelative kurtosis 5.5.9. Following step(5.5.8), perform peak redetectionusing the RR intervals. Also try to build, using a Katman filter, anarray of the positions of the peaks 5.5.10. Following step (5.5.5/5.5.9), perform fetal peak examination using the information about theRR intervals 5.5.11. Following step (5.5.10), build the fetal peaksarray 5.5.11.1.Time varying RR interval is extracted from the results5.5.11.2.A characteristic region in the previous signal is defined: aregion in which the RR interval curve is very smooth (which means thatthere is minimum falses in this region) - this region is called the seedregion 5.5.11.3.Perform region growing staring from the seed pointswhile including only points that are close to the local RR interval

TABLE 16 5.5.12.Perform peak examination and correction: 5.5.12.1. Stage1: find false negatives and Zigzags (caused by two consecutive, oppositemisdetections) 5.5.12.2. Stage 2: Find mis-positioning of the peaks bydetecting spikes in the updating RR curve

Publications cited throughout this document are hereby incorporated byreference in their entirety. Although the various aspects of theinvention have been illustrated above by reference to examples andpreferred embodiments, it will be appreciated that the scope of theinvention is defined not by the foregoing description but by thefollowing claims properly construed under principles of patent law.

What is claimed is:
 1. A system for monitoring maternal and fetalcardiac activity comprising: a. at least one electrocardiogram sensorconfigured to contact the skin of the abdomen of a pregnant humansubject and detect fetal and maternal cardiac electrical activity; b. atleast one acoustic sensor configured to contact the skin of the abdomenof a pregnant human subject and detect fetal and maternal cardiacelectrical activity; c. a garment configured to position and contact theat least one electrocardiogram sensor and the at lean one acousticsensor on the abdomen of the pregnant human subject; d. a specificallyprogrammed computer system comprising: at least one specialized computermachine, comprising: a non-transient memory, electronically storingparticular computer executable program code, and at least one computerprocessor which, when executing the particular prop code, becomes aspecifically programed computing processor that is configured to atleast perform the following operations: receiving raw Electrocardiogram(ECG) signals data from the at least one pair of ECG sensors; whereinthe at least one pair of ECG sensors is positioned in on an abdomen of apregnant human subject; wherein the raw ECG signals data comprise datarepresentative of a N number of raw ECG signals (raw N-ECG signals data)which are being acquired in real-time from the at least one pair of ECGsensors; digital signal filtering the raw ECG signals data to formfiltered N-ECG signals data having filtered N-ECG signals; detectingmaternal heart peaks in each of the filtered N-ECG signal in thefiltered N-ECG signals data; subtracting, from each of the filteredN-ECG signal of the filtered N-ECG signals data, the maternal ECGsignal, by utilizing at least one non-linear subtraction procedure toobtain corrected ECG signals data which comprise data representative ofa N number of corrected ECG signals (convicted N-ECG signals data),wherein the at least one non-linear subtraction procedure comprises:iteratively performing: i) dividing each filtered N-ECG signal of N-ECGsignals of the filtered N-ECG signals data into a second plurality ofECG signal segments,) wherein each ECG signal segment of the pluralityof ECG sisal segments corresponds to a beat interval of a fullheartbeat, and 2) wherein each beat interval is automatically determinedbased, at least in put on automatically detecting an onset value and anoffset value of such best interval; ii) modifying each of the pluralityof filtered N-ECG signal segments to form a plurality of modifiedfiltered N-ECG signal segments, wherein the modifying is performed usingat least one inverse optimization scheme based on a set of parameters,wherein values of the set of parameters is determined based on:iteratively performing: 1) defining a global template based on astandard heartbeat profile of an adult human being; 2) setting a set oftentative values for a local template for each filtered N-ECG signalsegment; and 3) utilizing at least one optimization scheme to determinean adaptive template for each filtered N-ECG signal segment based on thelocal template being matched to the global template within apre-determined similarity value; and iii) eliminating the modifiedsegments from each of the filtered N-ECG signals, by subtracting theadaptive template from the filtered N-ECG signal thereby generating eachcorrected ECG signal; extracting raw fetal ECG signals data from thefiltered N-ECG signals data based on the corrected ECG signals data,wherein the raw fetal ECG signals data comprises a N number of fetal ECGsignals (raw N-ECG fetal signals data); processing the raw N-ECG fetalsignals data to improve a signal-to-noise ratio of the N-ECG fetalsignals to form filtered N-ECG fetal signals data: detecting fetal heartpeaks in the filtered N-ECG fetal signals data; calculating, based ondetected fetal heart peaks, at least one of: i) fetal heart rate, ii)fetal heart curve, iii) beat-2-beat fetal heart rate, or iv) fetal heartrate variability; and outputting a result of the calculating operation;e. a specifically programmed computer system comprising: at least onespecialized computer machine, comprising: a non-transient memory,electronically storing particular computer executable program code; andat least one computer processor which, when executing the particularprogram code, becomes a specifically programed computing processor thatis configured to at least perform the following operations: receiving,by at least one computer processor executing specific programmableinstructions configured for the method, a plurality of Phonocardiogram(PCG) signals data inputs from a plurality of acoustic sensors; digitalsignal filtering, by the at least one computer processor, utilizing aplurality of bandpass filters, the plurality of PCG signals data inputsto form a plurality of filtered PCG outputs, wherein the plurality ofbandpass filters comprises a L number of bandpass filters, wherein eachbandpass filter outputs a K number of filtered PCG outputs; waveletdenoising, by the at least one computer processor, a first subset offiltered PCG outputs of the plurality of filtered PCG outputs to form aM number of denoised filtered PCG outputs, wherein M is equal to Lmultiply by K; transforming, by the at least one computer processor,utilizing an independent-Component-Analysis (ICA), a second subset offiltered PCG outputs of the plurality of filtered PCG outputs to formthe M number of filtered ICA transforms; transforming, by the at leastone computer processor, utilizing the independent-Component-Analysis(ICA), a first portion of the second subset of denoised filtered PCGoutputs to form the M number of denoised filtered ICA transforms;compiling, by the at least one computer processor, a S number of aplurality of detection heartbeat (DH) inputs, comprising: i) the Mnumber of filtered PCG outputs, ii) the M number of the denoisedfiltered PCG outputs, iii) the M number of the filtered ICA transforms,and iv) the M number of the denoised filtered ICA transforms; detecting,by the at least one computer processor, beat locations of beats in eachof DH inputs; calculating, by the at least one computer processor, aconfidence score that describes a probability that the beats in each DHinput of the plurality of DH inputs represent actual heartbeats and nota noise; dividing, by the at least one computer processor, the pluralityof DH inputs into at least two groups: i) a first group of DH inputscontaining fetal heartbeats, ii) a second group of DH inputs containingmaternal heartbeats; selecting, by the at least one computer processor,from the first group of DH inputs, at least one particular fetal DHinput that contains the fetal heartbeat based on a first confidencescore of the at least one particular fetal DH input; and selecting, bythe at least one computer processor, from the second group of DH inputs,at least one particular maternal DH input that contains the maternalheartbeat, based on a second confidence score of the at least oneparticular maternal DH input; f. a specifically programmed computersystem comprising: at least one specialized computer machine,comprising: a non-transient memory, electronically storing particularcomputer executable program code; and at least one computer processorwhich, when executing the particular program code, becomes aspecifically programmed computing processor that is configured to atleast perform the following operations: i. receiving a calculated fetalheart rate for a plurality of time points over a particular timeinterval from filtered N-ECG fetal signals data and a calculated fetalheart rate for a plurality of time points over a particular timeinterval from filtered PCG outputs; ii. determining the score of thecalculated fetal heart rate for the plurality of time points over theparticular time interval for the filtered N-ECG fetal signals; ii.determining the score of the calculated fetal heart rate for theplurality of time points over the particular time interval for thefiltered PCG outputs; iv. based on the calculated fetal heart rate andscore for a plurality of time points over a particular time intervalfrom filtered N-ECG fetal signals data, and the calculated fetal beatrate and score for a plurality of time points over a particular timeinterval from filtered PCG outputs determining a consolidated fetalheart rate and score for the plurality of time points over theparticular time interval, wherein the consolidated fetal heat rate andscore for an individual time point within the plurality of time pointsis determined as one of the four options selected from the groupconsisting of:
 1. the weighted average of the calculated heart rate fromthe filtered N-ECG fetal signals data and the filtered PCG outputs forthe individual time point, if the calculated heart rate from thefiltered N-ECG fetal signals data and the filtered PCG outputs for theindividual time point differs by 10 beats per minute or less, and if thescores of the calculated fetal heartrate for the individual time pointfor both the filtered N-ECG fetal signals data and the filtered PCGoutputs are valid;
 2. the calculated heart rate having the lower score,if the calculated heart rate from the filtered N-ECG fetal signals dataand the filtered PCG outputs for the individual time point differs bymore than 10 beats per minute, and if the scores of the calculated fetalheartrate for the individual time point for both the filtered N-ECGfetal signals data and the filtered PCG outputs are valid;
 3. thecalculated heart rate that has the valid score; and
 4. no consolidatedfetal heart rate and score, if neither the calculated heart rate fromthe filtered N-ECG fetal signals data or the filtered PCG outputs has avalid score; v. based on the consolidated heart rate and scores for theplurality of time points over the particular time interval, generating,by the at least one computer processor, a fetal heart rate probabilitymesh; vi. based on the fetal heart rate probability mesh, generating, bythe at least one computer processor, an estimated fetal heart rate overthe particular time interval, wherein the estimated fetal heat rate overthe particular time interval is calculated based on (1) costrepresenting fetal heart probability mesh values at each point of theestimated fetal heart rate over the particular time interval; and (2)cost representing the overall tortuosity of the estimated fetal heartrate over the particular time interval.
 2. The system of claim 1,wherein the garment comprises a belt.
 3. A system for generating anestimation of fetal cardiac activity, comprising: a. a specificallyprogrammed computer system comprising: at least one specialized computermachine, comprising: a non-transient memory, electronically storingparticular computer executable program code; and at least one computerprocessor which, when executing the particular program code, becomes aspecifically programmed computing processor that is configured to atleast perform the following operations: i. receiving a calculated fetalheart rate for a plurality of time points over a particular timeinterval from filtered N-ECG fetal signals data and a calculated fetalheart rate for a plurality of time points over a particular timeinterval from filtered PCG outputs; ii. determining the score of thecalculated fetal heart rate for the plurality of time points over theparticular time interval for the filtered N-ECG fetal signals; iii.determining the score of the calculated fetal heart rate for theplurality of time points over the particular time interval for thefiltered PCG outputs; iv. based on the calculated fetal heart rate andscore for a plurality of time points over a particular time intervalfrom filtered N-ECG fetal signals data, and the calculated fetal heartrate and score for a plurality of time points over a particular timeinterval from filtered PCG outputs, determining a consolidated fetalheart rate and score for the plurality of time points over theparticular time interval, wherein the consolidated fetal heart rate andscore for an individual time point within the plurality of time pointsis determined as one of the four options selected from the groupconsisting of: a. the weighted average of the calculated heart rate fromthe filtered N-ECG fetal signals data and the filtered PCG outputs forthe individual time point, if the calculated heart rate from thefiltered N-ECG fetal signals data and the filtered PCG outputs for theindividual time point differs by 10 beats per minute or less, and if thescores of the calculated fetal heart rate for the individual time pointfor both the filtered N-ECG fetal signals data and the filtered PCGoutputs are valid; b. the calculated heart rate having the lower score,if the calculated heart rate from the filtered N-ECG fetal signals dataand the filtered PCG outputs for the individual time point differs bymore than 10 beats per minute, and if the scores of the calculated fetalheart rate for the individual time point for both the filtered N-ECGfetal signals data and the filtered PCG outputs are valid; c. thecalculated heart rate that has the valid score; and d. no consolidatedfetal heart rate and score, if neither the calculated heart rate fromthe filtered N-ECG fetal signals data or the filtered PCG outputs has avalid score; v. based on the consolidated heart rate and scores for theplurality of time points over the particular time interval, generating,by the at least one computer processor, a fetal heart rate probabilitymesh; vi. based on the fetal heart rate probability mesh, generating, bythe at least one computer processor, an estimated fetal heart rate overthe particular time interval, wherein the estimated fetal heart rateover the particular time interval is calculated based on (1) costrepresenting fetal heart probability mesh values at each point of theestimated fetal heart rate over the particular time interval; and (2)cost representing the overall tortuosity of the estimated fetal heartrate over the particular time interval.
 4. The system of claim 3,wherein cost representing fetal heart probability mesh values at eachpoint of the estimated fetal heart rate over the particular timeinterval; and cost representing the overall tortuosity of the estimatedfetal heart rate over the particular time interval is performed, by theat least one computer processor, using dynamic programming.
 5. Thesystem of claim 4, wherein each value of the accumulated cost mesh iscalculated as a sum of the fetal heart rate probability mesh value atthat point and of the minimal path in its neighborhood in the previousstep, based on:E(i,j)=e(i,j)+min(E(i−1,j−k)); k=−4:4 wherein e is the value of thefetal heart rate probability mesh; E is the accumulated cost; irepresents time; and j represents heart rate values in neighborhood of+/−4 [bpm/second].
 6. The system of claim 3, wherein cost representingfetal heart probability mesh values at each point of the estimated fetalheart rate over the particular time interval; and cost representing theoverall tortuosity of the estimated fetal heart rate over the particulartime interval is performed, by the at least one computer processor,using an exhaustive search.
 7. A computer implemented method comprising:a. receiving raw Electrocardiogram (ECG) signals data from the at leastone pair of ECG sensors; wherein the at least one pair of ECG sensors ispositioned in on an abdomen of a pregnant human subject; wherein the rawECG signals data comprise data representative of a N number of raw ECGsignals (raw N-ECG signals data) which are being acquired in real-timefrom the at least one pair of ECG sensors; digital signal filtering theraw ECG signals data to form filtered N-ECG signals data having filteredN-ECG signals, detecting maternal heart peaks in each of the filteredN-ECG signal in the filtered N-ECG signals data; subtracting, from eachof the filtered N-ECG signal of the filtered N-ECG signals data, thematernal ECG signal, by utilizing at least one non-linear subtractionprocedure to obtain corrected ECG signals data which comprise datarepresentative of a N number of corrected ECG signals (corrected N-ECGsignals data), wherein the at least one non-linear subtraction procedurecomprises: iteratively performing: i) dividing each filtered N-ECGsignal of N-ECG signals of the filtered N-ECG signals data into a secondplurality of ECG signal segments,) wherein each ECG signal segment ofthe plurality of ECG signal segments corresponds to a beat interval of afull heartbeat, and 2) wherein each beat interval is automaticallydetermined based, at least in part on automatically detecting an onsetvalue and an offset value of such beat interval; ii) modifying each ofthe plurality of filtered N-ECG signal segments to form a plurality ofmodified filtered N-ECG signal segments, wherein the modifying isperformed using at least one inverse optimization scheme based on a setof parameters, wherein values of the set of parameters is determinedbased on: iteratively performing: 1) defining a global template based ona standard heartbeat profile of an adult human being; 2) setting a setof tentative values for a local template for each filtered N-ECG signalsegment; and 3) utilizing at least one optimization scheme to determinean adaptive template for each filtered N-ECG signal segment based on thelocal template being matched to the global template within apre-determined similarity value; and iii) eliminating the modifiedsegments from each of the filtered N-ECG signals, by subtracting theadaptive template from the filtered N-ECG signal thereby generating eachcorrected ECG signal; extracting raw fetal ECG signals data from thefiltered N-ECG signals data based on the corrected ECG signals data,wherein the raw fetal ECG signals data comprises a N number of fetal ECGsignals (raw N-ECG fetal signals data); processing the raw N-ECG fetalsignals data to improve a signal-to-noise ratio of the N-ECG fetalsignals to form filtered N-ECG fetal signals data; detecting fetal heartpeaks in the filtered N-ECG fetal signals data, calculating, based ondetected fetal heart peaks, at least one of: i) fetal heart rate, ii)fetal heart curve, iii) beat-2-beat fetal heart rate, or iv) fetal heartrate variability; and outputting a result of the calculating operation;b. receiving, by at least one computer processor executing specificprogrammable instructions configured for the method, a plurality ofPhonocardiogram (PCG) signals data inputs from a plurality of acousticsensors; digital signal filtering, by the at least one computerprocessor, utilizing a plurality of bandpass filters, the plurality ofPCG signals data inputs to form a plurality of filtered PCG outputs,wherein the plurality of bandpass filters comprises a L number ofbandpass filters, wherein each bandpass filter outputs a K number offiltered PCG outputs; wavelet denoising, by the at least one computerprocessor, a first subset of filtered PCG outputs of the plurality offiltered PCG outputs to form a M number of denoised filtered PCGoutputs, wherein M is equal to L multiply by K; transforming, by the atleast one computer processor, utilizing anIndependent-Component-Analysis (ICA), a second subset of filtered PCGoutputs of the plurality of filtered PCG outputs to form the M number offiltered ICA transforms; transforming, by the at least one computerprocessor, utilizing the Independent-Component-Analysis (ICA), a firstportion of the second subset of denoised filtered PCG outputs to formthe M number of denoised filtered ICA transforms; compiling, by the atleast one computer processor, a S number of a plurality of detectionheartbeat (DH) inputs, comprising: i) the M number of filtered PCGoutputs, ii) the M number of the denoised filtered PCG outputs, iii) theM number of the filtered ICA transforms, and iv) the M number of thedenoised filtered ICA transforms; detecting, by the at least onecomputer processor, beat locations of beats in each of DH inputs;calculating, by the at least one computer processor, a confidence scorethat describes a probability that the beats in each DH input of theplurality of DH inputs represent actual heartbeats and not a noise;dividing, by the at least one computer processor, the plurality of DHinputs into at least two groups: i) a first group of DH inputscontaining fetal heartbeats, ii) a second group of DH inputs containingmaternal heartbeats; selecting, by the at least one computer processor,from the first group of DH inputs, at least one particular fetal DHinput that contains the fetal heartbeat based on a first confidencescore of the at least one particular fetal DH input; and selecting, bythe at least one computer processor, from the second group of DH inputs,at least one particular maternal DH input that contains the maternalheartbeat, based on a second confidence score of the at least oneparticular maternal DH input; c. performing the following operations onthe results of step a and b: i. receiving a calculated fetal heart ratefor a plurality of time points over a particular time interval fromfiltered N-ECG fetal signals data and a calculated fetal heart rate fora plurality of time points over a particular time interval from filteredPCG outputs; ii. determining the score of the calculated fetal heartrate for the plurality of time points over the particular time intervalfor the filtered N-ECG fetal signals, iii. determining the score of thecalculated fetal heart rate for the plurality of time points over theparticular time interval for the filtered PCG outputs; iv. based on thecalculated fetal heart rate and score for a plurality of time pointsover a particular time interval from filtered N-ECG fetal signals data,and the calculated fetal heart rate and score for a plurality of timepoints over a particular time interval from filtered PCG outputs,determining a consolidated fetal heart rate and score for the pluralityof time points over the particular time interval, wherein theconsolidated fetal heart rate and score for an individual time pointwithin the plurality of time points is determined as one of the fouroptions selected from the group consisting of:
 1. the weighted averageof the calculated heart rate from the filtered N-ECG fetal signals dataand the filtered PCG outputs for the individual time point, if thecalculated heart rate from the filtered N-ECG fetal signals data and thefiltered PCG outputs for the individual time point differs by 10 beatsper minute or less, and if the scores of the calculated fetal heart ratefor the individual time point for both the filtered N-ECG fetal signalsdata and the filtered PCG outputs are valid;
 2. the calculated heartrate having the lower score, if the calculated heart rate from thefiltered N-ECG fetal signals data and the filtered PCG outputs for theindividual time point differs by more than 10 beats per minute, and ifthe scores of the calculated fetal heart rate for the individual timepoint for both the filtered N-ECG fetal signals data and the filteredPCG outputs are valid;
 3. the calculated heart rate that has the validscore; and
 4. no consolidated fetal heart rate and score, if neither thecalculated heart rate from the filtered N-ECG fetal signals data or thefiltered PCG outputs has a valid score; v. based on the consolidatedheart rate and scores for the plurality of time points over theparticular time interval, generating, by the at least one computerprocessor, a fetal heart rate probability mesh; vi. based on the fetalheart rate probability mesh, generating, by the at least one computerprocessor, an estimated fetal heart rate over the particular timeinterval, wherein the estimated fetal heart rate over the particulartime interval is calculated based on (1) cost representing fetal heartprobability mesh values at each point of the estimated fetal heart rateover the particular time interval; and (2) cost representing the overalltortuosity of the estimated fetal heart rate over the particular timeinterval.
 8. The method of claim 7, wherein cost representing fetalheart probability mesh values at each point of the estimated fetal heartrate over the particular time interval; and cost representing theoverall tortuosity of the estimated fetal heart rate over the particulartime interval is performed, by the at least one computer processor,using dynamic programming.
 9. The method of claim 8, wherein each valueof the accumulated cost mesh is calculated as a sum of the fetal heartrate probability mesh value at that point and of the minimal path in itsneighborhood in the previous step, based on:E(i,j)=e(i,j)+min(E(i−1,j−k)); k=−4:4 wherein e is the value of thefetal heart rate probability mesh; E is the accumulated cost; irepresents time; and j represents heart rate values in neighborhood of+/−4 [bpm/second].
 10. The method of claim 7, wherein cost representingfetal heart probability mesh values at each point of the estimated fetalheart rate over the particular time interval; and cost representing theoverall tortuosity of the estimated fetal heart rate over the particulartime interval is performed, by the at least one computer processor,using an exhaustive search.